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The Indo-Gangetic Plain (IGP) experiences severe air pollution every winter, with ammonium chloride and ammonium nitrate as the major inorganic fractions of fine aerosols. Many past attempts to tackle air pollution in the IGP were inadequate, as they targeted a subset of the primary pollutants in an environment where the majority of the particulate matter burden is secondary in nature. Here, we provide new mechanistic insight into aerosol mitigation by integrating the ISORROPIA-II thermodynamical model with high-resolution simultaneous measurements of precursor gases and aerosols. A mathematical framework is explored to investigate the complex interaction between hydrochloric acid (HCl), nitrogen oxides (NO x ), ammonia (NH 3 ), and aerosol liquid water content (ALWC). Aerosol acidity (pH) and ALWC emerge as governing factors that modulate the gas-to-particle phase partitioning and mass loading of fine aerosols. Six \"sensitivity regimes\" were defined, where PM 1 and PM 2.5 fall in the \"HCl and HNO 3 sensitive regime\", emphasizing that HCl and HNO 3 reductions would be the most effective pathway for aerosol mitigation in the IGP, which is ammonia-rich during winter. This study provides evidence that precursor abatement for aerosol mitigation should not be based on their descending mass concentrations but instead on their sensitivity to high aerosol loading.

We investigate the impact of adoption of electric vehicles and cleaner fuels on future surface levels of PM2.5 and ozone over Delhi for two contrasting seasons, pre-monsoon and post-monsoon. We run the WRF-Chem atmospheric transport model at high resolution (4 km) with two transport emission scenarios for year 2030: (1) a scenario with electrification of two- and three-wheelers and light commercial vehicles, and (2) a scenario which also includes conversion of diesel vehicles to compressed natural gas (CNG). Compared to the baseline values in 2019, the scenario with both electrification and conversion of diesel vehicles to CNG has a greater reduction in PM2.5 concentrations (up to 5%) than the electrification of two- and three-wheelers and light commercial vehicles alone (within 1%), mainly due to the the greater reduction in primary emissions of PM2.5 and black carbon from diesel conversion to CNG. Vehicles electrification could result in an increase in the daily maximum 8-hours ozone concentrations, which are partially offset by additionally converting to CNG—by −1.9% and +2.4% during pre-monsoon and post-monsoon seasons. This reflects higher NOx emissions from the CNG vehicle scenario compared to electrification-alone scenario, which limits the increase of surface ozone in the VOC-limited chemical environment over Delhi. Our findings highlight the importance of a coordinated strategy for PM2.5 and ozone when considering traffic emission controls, and highlight that the transition to electric vehicles should be accompanied by the conversion of diesel vehicles to CNG to limit surface ozone increase and achieve greater reduction in PM2.5 concentrations over Delhi. However, the small changes in PM2.5 and in ozone compared to the baseline scenario highlight the importance of joint emissions reduction from other sectors to achieve substantial progress in PM2.5 and ozone air quality in Delhi.

In this study we undertook quantitative source apportionment for 32 volatile organic compounds (VOCs) measured at a suburban site in the densely populated northwest Indo-Gangetic Plain using the US EPA PMF 5.0 model. Six sources were resolved by the PMF model. In descending order of their contribution to the total VOC burden these are “biofuel use and waste disposal” (23.2 %), “wheat-residue burning”(22.4 %), “cars” (16.2 %), “mixed daytime sources”(15.7 %) “industrial emissions and solvent use”(11.8 %), and “two-wheelers” (8.6 %). Wheat-residue burning is the largest contributor to the total ozone formation potential (32.4 %). For the emerging contaminant isocyanic acid, photochemical formation from precursors (37 %) and wheat-residue burning (25 %) were the largest contributors to human exposure. Wheat-residue burning was also the single largest source of the photochemical precursors of isocyanic acid, namely, formamide, acetamide and propanamide, indicating that this source must be most urgently targeted to reduce human concentration exposure to isocyanic acid in the month of May. Our results highlight that for accurate air quality forecasting and modeling it is essential that emissions are attributed only to the months in which the activity actually occurs. This is important for emissions from crop residue burning, which occur in May and from mid-October to the end of November. The SOA formation potential is dominated by cars (36.9 %) and two-wheelers (21.1 %), which also jointly account for 47% of the human class I carcinogen benzene in the PMF model. This stands in stark contrast to various emission inventories which estimate only a minor contribution of the transport sector to the benzene exposure (∼10 %) and consider residential biofuel use, agricultural residue burning and industry to be more important benzene sources. Overall it appears that none of the emission inventories represent the regional emissions in an ideal manner. Our PMF solution suggests that transport sector emissions may be underestimated by GAINSv5.0 and EDGARv4.3.2 and overestimated by REASv2.1, while the combined effect of residential biofuel use and waste disposal emissions as well as the VOC burden associated with solvent use and industrial sources may be overestimated by all emission inventories. The agricultural waste burning emissions of some of the detected compound groups (ketones, aldehydes and acids) appear to be missing in the EDGARv4.3.2 inventory.

A positive matrix factorization model (US EPA PMF version 5.0) was applied for the source apportionment of the dataset of 37 non-methane volatile organic compounds (NMVOCs) measured from 19 December 2012 to 30 January 2013 during the SusKat-ABC international air pollution measurement campaign using a proton-transfer-reaction time-of-flight mass spectrometer in the Kathmandu Valley. In all, eight source categories were identified with the PMF model using the new constrained model operation mode. Unresolved industrial emissions and traffic source factors were the major contributors to the total measured NMVOC mass loading (17.9 and 16.8 %, respectively) followed by mixed industrial emissions (14.0 %), while the remainder of the source was split approximately evenly between residential biofuel use and waste disposal (10.9 %), solvent evaporation (10.8 %), biomass co-fired brick kilns (10.4 %), biogenic emissions (10.0 %) and mixed daytime factor (9.2 %). Conditional probability function (CPF) analyses were performed to identify the physical locations associated with different sources. Source contributions to individual NMVOCs showed that biomass co-fired brick kilns significantly contribute to the elevated concentrations of several health relevant NMVOCs such as benzene. Despite the highly polluted conditions, biogenic emissions had the largest contribution (24.2 %) to the total daytime ozone production potential, even in winter, followed by solvent evaporation (20.2 %), traffic (15.0 %) and unresolved industrial emissions (14.3 %). Secondary organic aerosol (SOA) production had approximately equal contributions from biomass co-fired brick kilns (28.9 %) and traffic (28.2 %). Comparison of PMF results based on the in situ data versus REAS v2.1 and EDGAR v4.2 emission inventories showed that both the inventories underestimate the contribution of traffic and do not take the contribution of brick kilns into account. In addition, the REAS inventory overestimates the contribution of residential biofuel use and underestimates the contribution of solvent use and industrial sources in the Kathmandu Valley. The quantitative source apportionment of major NMVOC sources in the Kathmandu Valley based on this study will aid in improving hitherto largely un-validated bottom-up NMVOC emission inventories, enabling more focused mitigation measures and improved parameterizations in chemical transport models.

Winter fog and severe aerosol loading in the boundary layer over northern India, particularly in the Indo-Gangetic Plain (IGP), disrupt the daily lives of millions of people in the region. To better understand the role of aerosol–radiation (AR) feedback on the occurrence, spatial extent, and persistence of winter fog, as well as the associated aqueous chemistry in fog in the IGP, several model simulations have been performed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). While WRF-Chem was able to represent the fog formation for the 23–24 December 2017 fog event over the central IGP in comparison to station and satellite observations, the model underestimated PM2.5 concentrations compared to the Central Pollution Control Board (CPCB) of India monitoring network. While evaluating aerosol composition for fog events in the IGP, we found that the WRF-Chem aerosol composition was quite different from measurements obtained during the Winter Fog Experiment (WiFEX) in Delhi, with secondary aerosols, particularly the chloride aerosol fraction, being strongly underpredicted (∼ 66.6 %). Missing emission sources (e.g., industry and residential burning of cow dung and trash) and aerosol and chemistry processes need to be investigated to improve model–observation agreement. By investigating a fog event on 23–24 December 2017 over the central IGP, we found that the aerosol–radiation feedback weakens turbulence, lowers the boundary layer height, and increases PM2.5 concentrations and relative humidity (RH) within the boundary layer. Factors affecting the feedback include loss of aerosols through deposition of cloud droplets and internal mixing of absorbing and scattering aerosols. Aqueous-phase chemistry increases the PM2.5 concentrations, which subsequently affect the aerosol–radiation feedback by both increased mass concentrations and aerosol sizes. With aerosol–radiation interaction and aqueous-phase chemistry, fog formation began 1–2 h earlier and caused a longer fog duration than when these processes were not included in the WRF-Chem simulation. The increase in RH in both experiments was found to be important for fog formation as it promoted the growth of aerosol size through water uptake, increasing the fog water content over the IGP. The results from this study suggest that the aerosol–radiation feedback and secondary aerosol formation play an important role in the air quality and the intensity and lifetime of fog over the IGP, yet other feedbacks, such as aerosol–cloud interactions, need to be quantified.

Global sulfate production plays a key role in aerosol radiative forcing; more than half of this production occurs in clouds. We found that sulfur dioxide oxidation catalyzed by natural transition metal ions is the dominant in-cloud oxidation pathway. The pathway was observed to occur primarily on coarse mineral dust, so the sulfate produced will have a short lifetime and little direct or indirect climatic effect. Taking this into account will lead to large changes in estimates of the magnitude and spatial distribution of aerosol forcing. Therefore, this oxidation pathway-which is currently included in only one of the 12 major global climate models-will have a significant impact on assessments of current and future climate.

Volatile organic compounds (VOCs) and particulate matter (PM) are major constituents of smog. Delhi experiences severe smog during the post-monsoon season, but a quantitative understanding of VOCs and PM sources is still lacking. Here, we conduct a source apportionment study for VOCs and PM using a recent (2022), high-quality dataset of 111 VOCs, PM2.5, and PM10 in a positive matrix factorization (PMF) model. Contrasts between clean monsoon air and polluted post-monsoon air, VOC source fingerprints, and molecular tracers enabled us to differentiate paddy residue burning from other biomass-burning sources, which had previously been impossible. Burning of fresh paddy residue, as well as residential heating and waste burning, contributed the most to observed PM10 levels (25 % and 23 %, respectively) and PM2.5 levels (23 % and 24 %, respectively), followed by heavy-duty vehicles fuelled by compressed natural gas (CNG), with a PM10 contribution of 15 % and a PM2.5 contribution of 11 %. For ambient VOCs, ozone formation potential, and secondary-organic-aerosol (SOA) formation potential, the top sources were petrol four-wheelers (20 %, 25 %, and 30 %, respectively), petrol two-wheelers (14 %, 12 %, and 20 %, respectively), industrial emissions (12 %, 14 %, and 15 %, respectively), solid-fuel-based cooking (10 %, 10 %, and 8 %, respectively), and road construction (8 %, 6 %, and 9 %, respectively). Emission inventories tended to overestimate residential biofuel emissions at least by a factor of 2 relative to the PMF output. The major source of PM pollution was regional biomass burning, while traffic and industries governed VOC emissions and secondary-pollutant formation. Our novel source apportionment method even quantitatively resolved similar biomass and fossil fuel sources, offering insights into both VOC and PM sources affecting extreme pollution events. This approach represents a notable advancement compared to current source apportionment approaches, and it could be of great relevance for future studies in other polluted cities and regions of the world with complex source mixtures.

Biogenic volatile organic compounds exert a strong influence on regional air quality and climate through their roles in the chemical formation of ozone and fine-mode aerosol. Dimethyl sulfide (DMS), in particular, can also impact cloud formation and the radiative budget as it produces sulfate aerosols upon atmospheric oxidation. Recent studies have reported DMS emissions from terrestrial sources; however, their magnitudes have been too low to account for the observed ecosystem-scale DMS emission fluxes. Big-leaf mahogany (Swietenia macrophylla King) is an agroforestry and natural forest tree known for its high-quality timber and listed under the Convention on International Trade in Endangered Species (CITES). It is widely grown in the American and Asian environments (>2.4 million km2 collectively). Here, we investigated emissions of monoterpenes, isoprene and DMS as well as seasonal carbon assimilation from four big-leaf mahogany trees in their natural outdoor environment using a dynamic branch cuvette system, high-sensitivity proton transfer reaction mass spectrometer and cavity ring-down spectrometer. The emissions were characterized in terms of environmental response functions such as temperature, radiation and physiological growth phases including leaf area over the course of four seasons (summer, monsoon, post-monsoon, winter) in 2018–2019. We discovered remarkably high emissions of DMS (average in post-monsoon: ∼19 ng g−1 leaf dry weight h−1) relative to previous known tree DMS emissions, high monoterpenes (average in monsoon: ∼15 µg g−1 leaf dry weight h−1, which is comparable to oak trees) and low emissions of isoprene. Distinct linear relationships existed in the emissions of all three BVOCs with higher emissions during the reproductive phase (monsoon and post-monsoon seasons) and lower emissions in the vegetative phase (summer and winter seasons) for the same amount of cumulative assimilated carbon. Temperature and PAR dependency of the BVOC emissions enabled formulation of a new parameterization for use in global BVOC emission models. Using the measured seasonal emission fluxes, we provide the first estimates for the global emissions from mahogany trees which amount to circa 210–320 Gg yr−1 for monoterpenes, 370–550 Mg yr−1 for DMS and 1700–2600 Mg yr−1 for isoprene. Finally, through the results obtained in this study, we have been able to discover and identify mahogany as one of the missing natural sources of ambient DMS over the Amazon rainforest as well. These new emission findings, indication of seasonal patterns and estimates will be useful for initiating new studies to further improve the global BVOC terrestrial budget.

Volatile organic compounds (VOCs) significantly impact the atmospheric chemistry of polluted megacities. Delhi is a dynamically changing megacity, and yet our knowledge of its ambient VOC composition and chemistry is limited to few studies conducted mainly in winter before 2020 (all pre-COVID-19). Here, using a new extended volatility range high-mass-resolution (10 000–15 000) proton transfer reaction time-of-flight mass spectrometer, we measured and analysed ambient VOC mass spectra acquired continuously over a 4-month period, covering “clean” monsoon (July–September) and “polluted” post-monsoon seasons, for the year 2022. Out of 1126 peaks, 111 VOC species were identified unambiguously. Averaged total mass concentrations reached ∼ 260 µg m−3 and were > 4 times in the polluted season relative to the cleaner season, as driven by enhanced emissions from biomass burning and reduced atmospheric ventilation (∼ 2). Among 111, 56 were oxygenated, 10 contained nitrogen, 2 chlorine, 1 sulfur, and 42 were pure hydrocarbons. VOC levels during polluted periods were significantly higher than most developed world megacities. Methanethiol, dichlorobenzenes, C6 amides, and C9 organic acids/esters, which have previously never been reported in India, were detected in both the clean and polluted periods. The sources were industrial for methanethiol and dichlorobenzenes, purely photochemical for the C6 amides, and multiphase oxidation and partitioning for C9 organic acids. Aromatic VOC / CO emission ratio analyses indicated additional biomass combustion/industrial sources in the post-monsoon season, along with year-round traffic sources in both seasons. Overall, the unprecedented new information concerning ambient VOC speciation, abundance, variability, and emission characteristics during contrasting seasons significantly advances current atmospheric composition understanding of highly polluted urban atmospheric environments like Delhi.

The Winter Fog Experiment (WiFEX) was an intensive field campaign conducted at Indira Gandhi International Airport (IGIA) Delhi, India, in the Indo-Gangetic Plain (IGP) during the winter of 2017–2018. Here, we report the first comparison in South Asia of high-temporal-resolution simulation of ammonia (NH3) along with ammonium (NH4+) and total NHx (i.e., NH3+ NH4+) using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) and measurements made using the Monitor for AeRosols and Gases in Ambient Air (MARGA) at the WiFEX research site. In the present study, we incorporated the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosol scheme into WRF-Chem. Despite simulated total NHx values and variability often agreeing well with the observations, the model frequently simulated higher NH3 and lower NH4+ concentrations than the observations. Under the winter conditions of high relative humidity (RH) in Delhi, hydrogen chloride (HCl) was found to promote the increase in the particle fraction of NH4+ (which accounted for 49.5 % of the resolved aerosol in equivalent units), with chloride (Cl−) (29.7 %) as the primary anion. By contrast, the absence of chloride (HCl / Cl−) chemistry in the standard WRF-Chem model results in the prediction of sulfate (SO42-) as the dominant inorganic aerosol anion. To understand the mismatch associated with the fraction of NHx in the particulate phase (NH4+ / NHx), we added HCl / Cl− to the model and evaluated the influence of its chemistry by conducting three sensitivity experiments using the model: no HCl, base case HCl (using a published waste burning inventory), and 3 × base HCl run. We found that 3 × base HCl increased the simulated average NH4+ by 13.1 µg m−3 and NHx by 9.8 µg m−3 concentration while reducing the average NH3 by 3.2 µg m−3, which is more in accord with the measurements. Thus HCl / Cl− chemistry in the model increases total NHx concentration, which was further demonstrated by reducing NH3 emissions by a factor of 3 (−3 × NH3_EMI) in the 3 × base HCl simulation. Reducing NH3 emissions in the 3 × base HCl simulation successfully addressed the discrepancy between measured and modeled total NHx. We conclude that modeling the fate of NH3 in Delhi requires a correct chemistry mechanism accounting for chloride dynamics with accurate inventories of both NH3 and HCl emissions.