Sub-micron aerosols primarily comprise of organics, sulfate, nitrate, ammonium, and chloride. Of these, organics have high complexity in their chemical and physical characteristics. In this study, aerosol volatility of nonrefractory sub-micron aerosol was studied using a thermal denuder (at a constant temperature of 150 degrees C) coupled High-Resolution Time of flight Aerosol Mass Spectrometer (HR-TOF AMS) for the period from 6th to 26th February 2021. Nitrate and ammonium had the highest volatility (80 % and 68 %, respectively), followed by organics (54 %). The lowest volatility rate was observed for sulfate (26 %) indicating the tendency of sulfate aerosols to be more in the particle phase at higher temperatures. The volatility for organics decreases during afternoon and midnight hours due to the prevalence of secondary organic aerosol. Further positive matrix factorization (PMF) analysis on both the denuded and ambient organics aerosol revealed four factors, namely, hydrocarbon-like organic aerosols (HOA), biomass-burning organic aerosol (BBOA), semi-volatile oxygenated organic aerosol (SV-OOA), and low-volatile oxygenated organic aerosol (LV-OOA). Over the campaign, LV-OOA showed an increase in fractional contribution from 31 % to 56 % of total organics. SV-OOA, HOA, and BBOA showed decreasing trends, as they have higher volatility than LV-OOA. Surprisingly, SV-OOA exhibited higher volatility than that of HOA, and BBOA. Further, the volatility of oxygenated hydrocarbon peaks was compared with hydrocarbon peaks to assess the higher volatility of SV-OOA aerosols, and the oxygenated hydrocarbons were estimated to be more volatile than hydrocarbons. The present study highlighted the possible coating of SVOOA over the existing aerosols which may modulate the aerosol to cloud condensation nuclei (CCN) activation characteristics.

Emission sources influencing high particulate air pollution levels and related mortality in India have been studied earlier on country‐wide and sub‐national scales. Here, we use novel data sets of emissions (for 2019) and observations created under the Carbonaceous Aerosol Emissions, Source Apportionment, and Climate Impacts network in India (Venkataraman et al., 2020, https://doi.org/10.1175/bams‐d‐19‐0030.1) in WRF‐Chem simulations to evaluate drivers of high PM2.5 levels during episodes and in airsheds with different pollution levels. We identify airsheds in “extreme” (110–140 μg/m3), “severe” (80–110 μg/m3) and “significant” (40–80 μg/m3) exceedance of the Indian annual ambient air quality standard (National Ambient Air Quality Standards [NAAQS]) of 40 μg/m3 for PM2.5. We find that primary organic matter and anthropogenic mineral matter (largely coal fly‐ash) drive high PM2.5 levels, both annually and during high PM2.5 episodes. PM2.5 episodes are driven by organic aerosol in north India (Mohali) in wintertime but are additionally influenced by mineral matter and secondary inorganics in central (Bhopal), south India (Mysuru) and eastern India (Shyamnagar). Across airsheds in exceedance of the NAAQS and during high PM2.5 episodes, primary PM2.5 emissions arise largely from the residential sector (50%–75%). Formal sector emissions (industry, thermal power and transport; 40%–55%) drive airshed and episode scale PM2.5 exceedance in northern and eastern India. Agricultural residue burning emissions predominate (50%–75%) on episode scales, both in northern and central India, but not on annual scales. Interestingly, residential sector emissions strongly influence (60%–90%) airsheds in compliance with the NAAQS (annual mean PM2.5 < 40 μg/m3), implying the need for modern residential energy transitions for the reduction of ambient air pollution across India.

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI

Atmospheric new particle formation (NPF, via gas-to-particle conversion) occurs commonly in the troposphere which has implications for air quality, weather, and climate. Here, we comprehensively characterize NPF events at the High Altitude Cloud Physics Laboratory (HACPL) in a mountain semi-rural location, Mahabaleshwar using 2.5 years of semi-continuous measurements of ion and particle number size distributions. The occurrence frequency of NPF events was the maximum in March through May (pre-monsoon season) (22.9%) compared to other seasons. Considering all seasons at HACPL, the particle growth rates in the size range from 3 to 7 nm, 7-25 nm, and 5-25 nm varied from 0.5 to 6.9 nm h-1, 1.3-10.2 nm h-1, and 1.5-16.4 nm h-1, respectively. The formation rate of charged clusters (J+3 and J-3) are reported for the first time from India, which ranges from 3.1 to 294.4 cm-3 s- 1. The general absence of a positive correlation between the particle formation rate and the growth rate indicates that dissimilar vapors might have contributed to the formation and growth of particles. Further, the role of ions in particle formation was also reported for the first time in India, which indicates an insignificant contribution of ions to particle formation at HACPL. At higher temperatures and lower relative humidity conditions, the formation rates of neutral/charged particles are found to be higher. Overall, our results provide new insights into NPF event characterization in the Indian context which have critical importance to an improved process-level understanding of secondary aerosol formation processes globally.

The presence of organonitrate and organosulfate was found at Mahabaleshwar, a high altitude site, during the pre-monsoon season of 2016. A Time of Flight Aerosol Chemical Speciation Monitor (ToF-ACSM) was used to measure the organic and inorganic components of non-refractory particulate matter (NR-PM1) aerosol. Positive Matrix Factorization (PMF) was performed on the (i) organics mass spectra of the aerosol (PMFOA), (ii) organics mass spectra merged with inorganics (PMFOA+IOA) and (iii) integrated mass spectra of organics with NO+ and NO2+ ions (PMFOA+NOx) to derive the chemical information on organonitrate and organosulfate. The results of PMFOA were used as a reference for validating the factors obtained through the PMFOA+IOA and PMFOA+NOx results. The analysis of PMFOA resolved four PMF factors: hydrocarbon-like OA (HOA); biomass burning OA (BBOA); oxygenated OA-1 (OOA-1) and OOA-2. The analysis of PMFOA+IOA identified two additional inorganic factors: sulfate organic aerosol (OA) and nitrate OA. Sulfate OA and nitrate OA contributed 36% and 6%, respectively, to the total aerosol mass. Although both originated as secondary organic aerosol, they displayed different diurnal profiles. The results of PMFOA+NOx were used for the quantification and apportionment of nitrate aerosol in two forms, organic nitrate and inorganic nitrate, which contributed 38% and 62%, respectively, to the total nitrate aerosol mass. The diurnal variation in organic nitrate highlights photochemical oxidation and nocturnal oxidation by the nitrate radical as the two major sources. This source apportionment study using a combined (organic and inorganic) dataset provides new source factors and improves our understanding about the sources and chemical nature of submicron aerosols in the atmosphere. However, uncertainties in the quantification of organosulfate remain a limitation.

Unlike daily precipitation, the intensity variation of extreme rainfall events (EREs) on a sub -daily scale is governed by fast response dynamics through the variability of the vertical structure of thermodynamics. Understanding the causative mechanisms and controlling factors responsible for the rapid intensification of rainfall on the sub -daily scale over the West Coast (WC) of India is still inadequate. In this study, we investigated the subdaily scale variability of extreme rainfall events and possible regulating factors over the WC region, India. Our results suggest that, on average, the rainfall event spell lasts for -4 -6 days, having an intense rain rate distribution for a few hours (-3 -5 h). We observed that the nonlinear complex interaction among various regulating factors in a conducive environment responsively influences the extreme rain rate distribution on sub -daily scale over the WC region. Sub -daily scale rainfall durations of 1 -3, 3 -5, and 5 -15 h are crucial factors pertinent to EREs. Offshore trough -driven large-scale moisture loaded winds along the west coast, when endured with strong updrafts, enhance the ice growth mechanism and lead to high surface rain intensity. The mid -troposphere moistening during the peak stage of the EREs amplifies the hydrometeor growth mechanisms as revealed by cloud specific liquid/ice water content distribution and cloud radar reflectivity profiles. A slopy and narrow radar reflectivity pattern above zero degrees isotherm suggests a contrast cloud vertical structure during the intense phase of the EREs. Though the intense nature of precipitation is caused by warm and mixed -phased clouds, the mixed -phase clouds result in precipitation events of a relatively longer duration.

Seasonal variability of volatile organic compounds (VOCs) was studied using year-long observations (June 2019 - May 2020) over a high-altitude (1380m AMSL) forested site in the Western Ghats of India. Isoprene, a wellknown biogenic VOC, peaked during pre-monsoon. Temperature stress and higher photo synthetically active radiation (PAR) were the drivers behind isoprene emissions from the surrounding forested regions. The daytime average isoprene mixing ratio was 1.92 +/- 1.51 ppb during summer and the night time average was 0.02 +/- 0.01 ppb. A strong relationship between isoprene mixing ratio and ambient temperature was derived. Most of the anthropogenic VOCs like acetonitrile, benzene, toluene and xylene were found to be higher during winter. These anthropogenic VOCs mostly exhibited afternoon to evening peaks, which suggests the contribution by traffic emissions along with other combustion sources (biofuels) and vertical updraft of pollutants from the surrounding valley region. The toluene to benzene ratio (T/B) was >1 during the afternoon and evening hours in monsoon, whereas, aged air masses with lower T/B ratios (<0.7) prevailed during other seasons. Polar bi-variate plots reveal that toluene, xylene and tri-methyl benzene (TMB) were mostly emitted at the south-west of the mea- surement site, irrespective of the season, indicating a dominant local traffic related source throughout the year. The contribution of local emissions and regional background on two selected aromatic VOCs, toluene and xylene was estimated, revealing the dominance of local anthropogenic sources during monsoon and post monsoon. Positive matrix factorization revealed three source factors in all the seasons with one additional factor during winter and pre-monsoon. The first factor was constituted of toluene, benzene, xylene and TMB, and the T/B ratio was-1.2, indicating traffic/fossil fuel contribution. A second 'biomass burning ' factor was dominated by acetonitrile, ethanal, acetone, and benzene in which the T/B ratio was 0.45. Factors one & two were further validated by comparison with chamber experiments conducted using different combustion sources and solvents. The third factor was dominated by isoprene and monoterpenes which were attributed to biogenic emissions. The fourth factor comprised mainly of acetonitrile, ethanal, and acetone indicating aged air masses due to long-range transport with contributions from photochemical production and biomass burning.

The paper aims to highlight the contrasting inter-seasonal variability of raindrop size distribution (DSD) as observed over Mumbai (representing a coastal city) and Mahabaleshwar (representing an orographic station in the Western Ghat mountain range) of Indian peninsula for a continuous period of four years (2018–2022). Upon examining the microphysical features of precipitation patterns, it is observed that raindrops with diameter of 3 mm and above dominate the rainfall in Mahabaleshwar during the pre-monsoon period, while the same with diameter of 1.5 mm and above dominate Mumbai's rainfall during the monsoon months. Additionally, the study finds a strong diurnal variation in rainfall occurrences during the pre-monsoon period for both the stations, while such variation is absent during the monsoon period. The analysis suggests that the Convective Available Potential Energy (CAPE) plays a significant role in these divergent diurnal patterns. Furthermore, the paper explores the relationship between mass-weighted mean diameter (Dm) - rain rate (R) values and shape (μ) versus slope (Λ) parameters as obtained from the two stations during the inter-seasonal phases of the monsoon. The results indicate that, during the pre-monsoon period, higher rain intensities (>16 mm/hr) correspond to a dominant Dm value in Mahabaleshwar's rainfall. Conversely, during the monsoon months, higher Dm values are noticeable in Mumbai's rainfall. Thus by connecting with all the analysis done through this paper, it can be said that convective rainfall dominates the pre-monsoon period over orographic station, with convectivity becoming evident at rain rates greater than 16 mm/hr. On the other hand, during the monsoon period, local thermodynamic conditions and higher moisture availability trigger the formation of deep convective clouds over coastal areas, thereby resulting for more convective rainfall in the coastal city compared to the orographic station.

The impact of a severe dust storm that originated over the Arabian Peninsula (AP) and travelled to the Indian subcontinent during 20-26 January 2022 is examined. According to the event's synoptic analysis, the Gulf of Oman, adjacent areas of Oman and Iran had strong mid-lower tropospheric westerlies because of the configuration of north-south cyclonic and anticyclonic circulation patterns generated by a sizable north-south pressure gradient. A strong westerly wind component continued down to surface level and brought large quantities of dust from the desert region of the Middle East to the Indian subcontinent. Overall, 90% of the monitoring locations over the Indian subcontinent (mostly the western region) exceeded the national tolerable level of 100 mu g m(-3) (PM10), with peaks as high as 650 mu g m(-3). Changes in optical and physical properties of aerosols varied in accordance with the dust loading, in which absorption aerosol optical depth denotes a 10% increase in absorbing aerosols. Due to the excessive cooling effect, the near-surface air temperature dropped by -6 degrees C from its daily climatology over a sizable zone of high aerosol loading. Additionally, the dust storm prolonged the winter's harshness by lowering temperatures (from pre-dust days) in some of the most severely afflicted areas to 10-12 degrees C. Changes in wind patterns at mid- and low levels have resulted in a temperature inversion (similar to 2.5 km), which prevented trapped dust particles from being diluted, which in turn increased the likelihood of a more significant decline in air quality and affecting human health/wealth.

Information on the spatial distribution of rainfall is required for many applications, including water and flood management. Weather radars can provide quantitative rainfall estimation over an area. Taking the example of an X-band radar installed at Mandhardev in the Western Ghats, we show that radar reflectivity data can have significant bias despite following standard calibration procedures. We report a technique to identify bias in the X-band radar reflectivity factor using collocated disdrometer observations and propose a methodology to correct it. Simultaneous data collected on 83 days during June–September of 2018 are used. Our results show that bias in the radar reflectivity factor reduced from –8.3 to –0.8 dB after correction. The estimated 83-day accumulated rainfall using uncorrected radar reflectivity data is 80

Aerosol physiochemical properties over a varied mining plateau region at the eastern end of a monsoon trough are reported for the first time and analyzed at different time scales. Aerosol optical depth (single scattering albedo, SSA) is found to be 0.49 (0.9) in pre-monsoon, 0.4 (0.94) in monsoon, 0.46 (0.92) in post-monsoon, and 0.36 (0.89) in winter, with an annual mean of 0.43 (0.91). The volume-size distribution is tri-modal, with 0.02 (ultra-fine), 0.2 (accumulation) and 7 (coarse) µm, but with seasonal signatures. The angstrom exponent (AE) varies along with the AOD, especially in winter, although they are inversely related to each other during monsoons; the increase in size may be due to the effect of humidity. AODbc varies between 13.4

The knowledge of type of precipitating cloud is crucial for radar based quantitative estimates of precipitation. We propose a novel model called CloudSense which uses machine learning to accurately identify the type of precipitating clouds over the complex terrain locations in the Western Ghats (WG) of India. CloudSense uses vertical reflectivity profiles collected during July-August 2018 from an X-band radar to classify clouds into four categories namely stratiform, mixed stratiform-convective, convective and shallow clouds. The machine learning (ML) model used in CloudSense was trained using a dataset balanced by Synthetic Minority Oversampling Technique (SMOTE), with features selected based on physical characteristics relevant to different cloud types. Among various ML models evaluated Light Gradient Boosting Machine (LightGBM) demonstrate superior performance in classifying cloud types with a BAC (Balanced Accuracy) of 0.79 and F1-Score of 0.8. CloudSense generated results are also compared against conventional radar algorithms and we find that CloudSense performs better than radar algorithms. For 200 samples tested, the radar algorithm achieved a BAC of 0.69 and F1-Score of 0.68, whereas CloudSense achieved a BAC of 0.8 and F1-Score of 0.79. Our results show that ML based approach can provide more accurate cloud detection and classification which would be useful to improve precipitation estimates over the complex terrain of the WG.

This study focuses on the chemical composition of cloud water (CW) and rainwater (RW) collected at Sinhagad, a high-altitude station (1450 m AMSL) located in the western region of India. The samples were collected during the monsoon over two years (2016–2017). The chemical analysis suggests that the concentration of total ionic constituents was three times higher in CW than in RW, except for NH4+ (1.0) and HCO3− (0.6). Compared to RW, high concentrations of SO42− and NO3− were observed in CW. The weighted average RW pH (6.5 ± 0.3) was slightly more alkaline than CW pH (6.1 ± 0.5). This can be attributed to the high concentrations of neutralizing ions such as nss-Ca2+, nss-Mg2+, K+, and NH4+, indicating the greater extent of wet scavenging during rainfall. These ions counteract the acidity generated by SO42− and NO3−. A high correlation between Ca2+, Na+, K+, NO3−, and SO42− makes it difficult to estimate the contribution of SO42− from different sources. Anthropogenic sulfur emissions and soil dust significantly influence the ionic composition of clouds and rain. Positive matrix factorization (PMF) was used to identify the contribution of different sources to the samples. In the CW, the extracted factors were cooking and vehicles, aging sea salt, agriculture, and dust. In RW, the factors were industries, cooking and vehicles, agriculture and dust, and aging sea salt. The findings of this study have significant implications for the monsoon build-up, ecosystems, agriculture, and climate change.

We examine the role of aerosol hygroscopicity (κ) affects clouds and precipitation formation over the Western Ghats (WG) in India using various numerical model simulations (i.e., particle-by-particle based small-scale, high resolution mesoscale model). For the diffusional growth of cloud droplets, the size dependent hygroscopicity is used in κ- Köhler equation of direct numerical simulation. The results of the small-scale model reveal that the distribution of cloud drop size varies from the initial mixing state to well mix state due to variation in κ. The value of κ is obtained from HTDMA instruments at High Altitude Cloud Physics Laboratory, India. The idealized and real simulations using WRF model with aerosol-aware Thompson microphysics scheme are conducted by changing κ values. Depending on the type of clouds (shallow or deep), different κ values determine the mass, number and precipitation of cloud and rain droplets. Low hygroscopicity (organics) simulates more and smaller drops, as well as uplifts below freezing level, resulting in more ice phase hydrometeors. Organic aerosols have a significant impact on the formation of more snow and graupel hydrometeors. As compared to high κ, low hygroscopicity weakens updrafts at the intermediate level and strengthens them at the upper level in the deep cloud region. The intensity of precipitation varies due to low and high κ. The findings indicate that aerosol composition has a significant impact on the activation of cloud condensation nuclei. This study suggests that aerosol hygroscopicity is essential in weather prediction models in order to integrate aerosol chemical compositions.

Light-absorbing carbonaceous aerosols that dominate atmospheric aerosol warming over India remain poorly characterized. Here, we delve into UV-visible-IR spectral aerosol absorption properties at nine PAN-India COALESCE network sites (Venkataraman et al., 2020, ). Absorption properties were estimated from aerosol-laden polytetrafluoroethylene filters using a well-constrained technique incorporating filter-to-particle correction factors. The measurements revealed spatiotemporal heterogeneity in spectral intrinsic and extrinsic absorption properties. Absorption analysis at near-UV wavelengths from carbonaceous aerosols at these regional sites revealed large near-ultraviolet brown carbon absorption contributions from 21% to 68%-emphasizing the need to include these particles in climate models. Further, satellite-retrieved column-integrated absorption was dominated by surface absorption, which opens possibilities of using satellite measurements to model surface-layer optical properties (limited to specific sites) at a higher spatial resolution. Both the satellite-modeled and direct in-situ absorption measurements can aid in validating and constraining climate modeling efforts that suffer from absorption underestimations and high uncertainties in radiative forcing estimates. Particulate pollution in the atmosphere scatter and absorb incoming solar energy, thus cooling or warming Earth's atmosphere. In developing countries and especially in India, one of the most polluted regions of the world, the extent to which particles can absorb solar energy and warm the atmosphere is not well understood. Here, for the first time, we measure particle absorption simultaneously at nine ground sites across India, in diverse geographical regions with different levels and types of particulate pollution. We find that organic carbon particles exert large absorption at near-ultraviolet wavelengths, which contain significant solar energy. These light absorbing organic carbon particles, called brown carbon, are emitted in large quantities from biomass burning (e.g., burning crop residue and cooking on wood-fired stoves). Comparing ground measurements of absorption with satellite-retrieved measurements that are representative of the entire atmospheric column, we find that near-surface atmospheric particles can exert significant warming. This study highlights the need to improve climate model simulations of particulate pollution's impact on the climate by incorporating spatiotemporal surface-level absorption measurements, including absorption by brown carbon particles. Measurements at nine regional PAN-India sites reveal several regions with large aerosol absorption strength Brown carbon contributes significantly (21%-68%) to near-ultraviolet absorption, indicating its importance in shortwave light absorption Strong correlations observed between satellite data and surface absorption indicate future potential in modeling surface absorption

Chemical composition of aerosols is of great concern in the Arctic because of its great influence on climate. In this communication, we report the physico-chemical properties of size-separated aerosol data archived at Gruvebadet lab in Ny-& Aring;lesund (78.55 degrees S, 11.55 degrees E) as a part of the Indian Arctic Mission over the station "Himadri" in 2010. The results reveal that the mass-size distribution (MSD) of aerosol composition exhibits tri-modal distribution with coarse-mode (62%), fine-mode (32%) and weak nucleation-mode (6%) indicating dominance of natural sources over the study region. MSD of chemical components showed a significant contribution to coarse-mode particles for Ca2+ ,Mg2+ ,Na+ and Cl -; fine-mode particles for SO42 -, NO3 -, NH4 + and K + . The marine sources contributed maximum for SO 42- (89%) and Mg2+ (44%) in the coarse fraction, and in the fine fraction, 31% to SO 42- and 86% to Mg2+. Non-marine sources were major contributors (80 to 95%) in both mode fractions for Ca2 + and K + . The estimated aerosol radiative forcing in the atmosphere of similar to 3.21 W/m2 could be attributed to the loading of black carbon aerosols (62%) over the site. The backward trajectories show air masses from Canada and Greenland travelling from 6000 m elevation, bringing the pollutants to Ny-& Aring;lesund and lower altitudes; the oceanic region within Arctic circle contributes more.

Combination of radiosonde profiles with collocated in-situ ground and aircraft measurements is used for the first time to study the vertical structure and microphysics of clouds during southwest monsoon over the Western Ghats, India. The morphology of clouds is detailed with the help of radiosonde observations and classified as low, mid, and high-level clouds depending on the cloud base height. Radiosonde sounding profiles indicated occur-rences of both single and multi-layered clouds with higher occurrences of single-layered (-35%) clouds during monsoon transition period (June and September) and two-layered (-42%) during core monsoon period (July and August). Dominance of low (-30%) and high-level (-60%) clouds were noticed compared to mid-level clouds over the observational site during the southwest monsoon.Warm cloud microphysics was investigated using collocated ground and airborne in situ measurements. Irrespective of the cloud type, the cloud liquid water content and the effective droplet diameter increased with altitude. One of the key results is the rapid broadening of the cloud droplet size distribution with height. The number concentration of droplets above 25 mu m diameters showed a steep decrease at altitudes above 1800 m, suggesting active collision-coalescence.