Concentrations of fine particulate matter (PM2.5) that exceed air quality standards affect human health and have an impact on the earth's radiation budget. The lack of round the clock ground-based observations from a dense network of air quality stations inhibits the understanding of PM2.5's spatio-temporal variability and the assessment of its health and climate effects. Aerosol optical depth (AOD) values retrieved from satellite based instruments can be used to derive surface PM2.5 concentrations. This study integrates Moderate Resolution Imaging Spectroradiometer (MODIS) AOD retrievals and simulations from the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) to determine the ground-level PM2.5 concentrations at a 36 km resolution across India. WRF-Chem simulations provide the factor relating the AOD with the PM2.5. Satellite-derived PM2.5 mass concentrations are compared with the available ground-based observations across India for the year of 2011. The results show a correlation between the satellite-derived monthly PM2.5 estimates and the ground-based observations for 15 stations in India with coefficients of 77% and diurnal scale coefficients varying from 0.45 to 0.75. The best estimations of PM2.5 mass concentrations on a spatio-temporal scale across India address various environmental issues.

The frequency and intensity of fog episodes during the winter season has been increasing during the past decade over the megacity of Delhi due to the high pollution load. The role of atmospheric aerosols is very important in the life cycle of fog in the urban areas. This paper presents the results on the variation in aerosol optical properties (scattering and absorption coefficients) and the black carbon (BC) mass concentration during the foggy period in winter (December 2015 to February 2016) at the Indira Gandhi International (IGI) Airport, New Delhi. The interaction between scattering and absorbing aerosols, and fog before, during and after the foggy period has been studied as a typical case. The BC mass concentration, along with the aerosol scattering and absorption coefficients, increased before and during the initial phase of the dense foggy period. However, there was a steep decrease in them after the sustained period of dense fog, which suggests possible scavenging by fog droplets. Also, it was observed that the decrease in ambient temperature and depression temperature (DT) and the increase in relative humidity (RH) played a major role in sustaining the dense fog despite the reduction in aerosol load. The single-scattering albedo (SSA) decreased during the dense fog due to a higher reduction of the scattering aerosols than the absorbing ones. Both the scattering and the absorption coefficients showed a significant correlation with cloud condensation nuclei (CCN).

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.

The presence of persistent heavy fog in northern India during winter creates hazardous situations for transportation systems and disrupts the lives of about 400 million people. The meteorological factors responsible for its genesis and predictability are not yet completely understood in this region. Given its high potential for socioeconomic impact, there is a pressing need for extensive research that understands the inherently complex nature of the phenomena through field observations and modeling exercises. WiFEX is a first-of-its-kind multi-institutional initiative dealing with intensive ground-based measurement campaigns for developing a suitable fog forecasting capability under the aegis of the smart cities mission of India. Measuring campaigns were conducted during the 2015-20 winters at the Indira Gandhi International Airport, New Delhi, covering more than 90 dense fog events. The field experiments involved extensive suites of in situ instruments and gathered simultaneous observations of micrometeorological conditions, radiative fluxes, turbulence, droplet/aerosol microphysics, aerosol optical properties, fog water chemistry, and vertical thermodynamical structure to describe the environmental stability in which fog develops. An operational modeling framework, the WRF Model, was set up to provide fog predictions during the measurement campaign. These field observations helped to interpret the strengths and deficiencies in the numerical modeling framework. Four scientific objectives were pursued: (i) the life cycle of optically thin and thick fog, (ii) microphysical properties in the polluted boundary layer, (iii) fog water chemistry, gas-aerosol partitioning during the fog life cycle, and (iv) numerical prediction of fog. This paper presents an overview of WiFEX and a synthesis of selected observational and modeling analyses/findings related to the abovementioned scientific topics.

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 (NOx), ammonia (NH3), 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 PM1 and PM2.5 fall in the "HCl and HNO3 sensitive regime", emphasizing that HCl and HNO3 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.

In patients with neuroendocrine tumors with liver metastases (NETLMs), complete resection of both the primary and liver metastases is a potentially curative option. When complete resection is not possible, debulking of the tumour burden has been proposed to prolong survival. The objective of this systematic review was to evaluate the effect of curative surgery (R0-R1) and debulking surgery (R2) on overall survival (OS) in NETLMs. For the subgroup of R2 resections, outcomes were compared by the degree of hepatic debulking (≥ 90% or ≥ 70%). A systematic review of the literature was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines using PubMed, Medline, CINAHL, Cochrane, and Embase databases. Hazard ratios (HRs) were estimated for each study and pooled using a random-effects inverse-variance meta-analysis model. Of 538 articles retrieved, 11 studies (1,729 patients) reported comparisons between curative and debulking surgeries. After pooling these studies, OS was found to be significantly shorter in debulking resections, with an HR of 3.49 (95% confidence interval, 2.70-4.51; p < 0.001). Five studies (654 patients) compared outcomes between ≥ 90% and ≥ 70% hepatic debulking approaches. Whilst these studies reported a tendency for OS and progression-free survival to be shorter in those with a lower degree of debulking, they did not report sufficient data for this to be assessed in a formal meta-analysis. In patients with NETLM, OS following surgical resection is the best to achieve R0-R1 resection. There is also evidence for a progressive reduction in survival benefit with lesser debulking of tumour load.

This paper discusses the comparative results of surface and satellite measurements made during the Phase1 (25 March to 14 April), Phase2 (15 April to 3 May) and Phase3 (3 May to 17May) of Covid-19 imposed lockdown periods of 2020 and those of the same locations and periods during 2019 over India. These comparative analyses are performed for Indian states and Tier 1 megacities where economic activities have been severely affected with the nationwide lockdown. The focus is on changes in the surface concentration of sulfur dioxide (SO2), carbon monoxide (CO), PM2.5 and PM10, Ozone (O3), Nitrogen dioxide (NO2) and retrieved columnar NO2 from TROPOMI and Aerosol Optical Depth (AOD) from MODIS satellite. Surface concentrations of PM2.5 were reduced by 30.59%, 31.64% and 37.06%, PM10 by 40.64%, 44.95% and 46.58%, SO2 by 16.73%, 12.13% and 6.71%, columnar NO2 by 46.34%, 45.82% and 39.58% and CO by 45.08%, 41.51% and 60.45% during lockdown periods of Phase1, Phase2 and Phase3 respectively as compared to those of 2019 periods over India. During 1st phase of lockdown, model simulated PM2.5 shows overestimations to those of observed PM2.5 mass concentrations. The model underestimates the PM2.5 to those of without reduction before lockdown and 1st phase of lockdown periods. The reduction in emissions of PM2.5, PM10, CO and columnar NO2 are discussed with the surface transportation mobility maps during the study periods. Reduction in the emissions based on the observed reduction in the surface mobility data, the model showed excellent skills in capturing the observed PM2.5 concentrations. Nevertheless, during the 1st & 3rd phases of lockdown periods AOD reduced by 5 to 40%. Surface O3 was increased by 1.52% and 5.91% during 1st and 3rd Phases of lockdown periods respectively, while decreased by -8.29% during 2nd Phase of lockdown period.

Abstract. The Winter Fog Experiment (WiFEX) was an intensive field campaign conducted at Indira Gandhi International Airport (IGIA) Delhi, India, in the Indo-Gangetic Plain during the winter of 2017–2018. Here, we report the first comparison in South Asia of the high temporal resolution measurements of NH3 along with water-soluble inorganic ions in PM2.5 (Cl-, NO3-, SO42- and NH4+) and corresponding precursor gases (HCl, SO2, HONO, and HNO3) made at the WiFEX research site, using the Monitor for AeRosols and Gases in Ambient Air (MARGA) and high-resolution simulations with Weather Research and Forecasting model coupled with chemistry (WRF-Chem). The hourly measurements were used to investigate how well the model captures the temporal variation of gaseous and particulate water-soluble species and gas-to-particle partitioning of NH3, using the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosol scheme. The model frequently simulated higher NH3 and lower NH4+ concentrations than the observations, while total NHx values/variability agreed well with the observations. Under the winter conditions of Delhi, high concentrations of hydrochloric acid (HCl) in the ambient air are found to dominate the gas-to-particle partitioning, as NH3 is usually in excess. The default model set-up of WRF-Chem excludes anthropogenic HCl emissions, so sulfuric acid (H2SO4) dominates the gas-to-particle partitioning with NH3 during the simulation period. The sensitivity experiments, including HCl emissions in the model, showed that the inclusion of HCl emissions improves the simulated gas-to-particle conversion rate of ammonia by 24 % (as indicated by NH4+ concentrations) while reducing the bias in gas phase NH3 by 10 %. Nevertheless, even with waste burning HCl emissions included, we find that WRF-Chem still overestimates sulfur dioxide (SO2) and nitrate (NO3−) formation and underestimates sulfate (SO42−), nitrous acid (HONO), nitric acid (HNO3), and HCl concentration in which it interacts, thus limit the gas-to-particle conversion of NH3 to NH4+ in the model. This indicates that modeling of ammonia requires a correct chemistry mechanism with accurate emission inventories for the industrial HCl emissions.

The campaign mode observational program 'Winter Fog Experiment' (WiFEX) was set up at the Indira Gandhi International Airport (IGIA), New Delhi, during the winter months of 2016–17 and 2017–18. Using the WiFEX data, in this study, we examine the microphysical structure of fog formed in a polluted environment and attempt to predict visibility ( V is ) using the fog index approach. The examination of eleven fog events demonstrates that the mean droplet concentration (up to 674.94 #/cm −3 ) and liquid water content (LWC, up to 0.29 g m −3 ) are high in dense fog cases ( V is < 200 m). The droplet spectrum shows bi-modal distribution and dominance of smaller droplets in the 3–7 µm range. For most fog cases, the droplet spectrum extends up to 50 µm. The mature phase of the fog depicts a relatively increased population of droplets in the higher-sized bins, highlighting the formation of larger droplets. Moreover, we found that V is is inversely related to the liquid water content and the fog droplet number concentration. Fog index-based visibility parameterization has been developed to diagnostically compute visibility for the different categories of fog events, namely category-IIIB (CAT-IIIB) and category-IIIC (CAT-IIIC), using the meteorological variables. Out of 14 CAT-IIIB and 19 CAT-IIIC fog events, the 'WiFEX-in' could predict seven CAT-IIIB and 12 CAT-IIIC fog events, respectively. However, significant under-prediction was evident for the total CAT-IIIB fog hours and over-prediction for the total CAT-IIIC fog hours. It is found that the observed and predicted fog hour differences were related to the errors in the fog onset, dissipation, and magnitude of predicted liquid water content during CAT-IIIB and CAT-IIIC events and the same are discussed.

Dense fog events and their micrometeorological characteristics and structural evolution at Indira Gandhi International Airport (IGIA), New Delhi, during the Winter Fog Experiment (WiFEX) are illustrated in this study. Four dense fog events that occurred in January 2016 for which visibility dropped below 200 m have been selected. Depending on the visibility and micrometeorological structure, the fog processes were classified into (i) an initial formation as a thermally stable optically thin fog and (ii) a subsequent mature, weakly unstable deep fog. Surface radiative cooling supported by a deep saturated layer in the nocturnal surface layer promotes the rapid development and intensification of the initial shallow fog into the extremely dense fog. Optically thin fog appeared to develop when a thin saturated layer of air formed near the ground under low-turbulence kinetic energy (< 0.1 m2/s2). The fog was sustained in the optically thin phase until the air at 20 m remained in a sub-saturated condition in the thermally stable surface layer. Furthermore, when the saturated layer near the surface progressively expanded upward as a result of sustained cooling inside the shallow fog, it rapidly transformed into an extremely dense fog. The threshold for transition from the optically thin phase to extremely dense phase appeared when the air at 20 m neared the saturation point. The dense fog observations for all cases indicate that when the saturated layer was deeper than 20 m, the fog was able to withstand larger turbulence intensity (TKE values between 0.4 m2/s2 and 0.5 m2/s2).

The Indo-Gangetic Plain (IGP) has high wintertime fine aerosol loadings that significantly modulate the widespread fog formation and sustenance. Here, we investigate the potential formation of secondary inorganic aerosol driven by excess ammonia during winter fog. Physicochemical properties of fine aerosols (PM1 and PM2.5) and trace gases (HCl, HONO, HNO3, SO2, and NH3) were simultaneously monitored at hourly resolution using Monitor for AeRosols and Gases in Ambient air (MARGA-2S) for the first time in India. Results showed that four major ions, i.e., Cl-, NO3-, SO42-, and NH4+ contributed approximately 97% of the total measured inorganic ionic mass. The atmosphere was ammonia-rich in winter and ammonium was the dominant neutralizer with aerosol neutralization ratio (ANR) close to unity. The correlation between ammonium and chloride was >0.8, implying the significant formation of ammonium chloride during fog in Delhi. Thermodynamical model ISORROPIA-II showed the predicted PM1 and PM2.5 pH to be 4.49 +/- 0.53, and 4.58 +/- 0.48 respectively which were in good agreement with measurements. The ALWC increased from non-foggy to foggy periods and a considerable fraction of fine aerosol mass existed in the supermicron size range of 1-2.5 mu m. The sulfur oxidation ratio (SOR) of PM1, PM2.5 reached up to 0.60, 0.75 in dense fog and 0.74, 0.87 when ambient RH crossed a threshold of 95%, much higher than non-foggy periods (with confidence level of >= 95%) pointing to enhanced formation of secondary aerosol in fog.

Abstract. Elevated levels of fine particulate matter (PM2.5) during winter-time have become one of the most important environmental concerns over the Indo-Gangetic Plain (IGP) region of India, and particularly for Delhi. Accurate reconstruction of PM2.5, its optical properties, and dominant chemical components over this region is essential to evaluate the performance of the air quality models. In this study, we investigated the effect of three different aerosol mechanisms coupled with gas-phase chemical schemes on simulated PM2.5 mass concentration in Delhi using the Weather Research and Forecasting model with the Chemistry module (WRF-Chem). The model was employed to cover the entire northern region of India at 10 km horizontal spacing. Results were compared with comprehensive filed data set on dominant PM2.5 chemical compounds from the Winter Fog Experiment (WiFEX) at Delhi, and surface PM2.5 observations in Delhi (17 sites), Punjab (3 sites), Haryana (4 sites), Uttar Pradesh (7 sites) and Rajasthan (17 sites). The Model for Ozone and Related Chemical Tracers (MOZART-4) gas-phase chemical mechanism coupled with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) aerosol scheme (MOZART-GOCART) were selected in the first experiment as it is currently employed in the operational air quality forecasting system of Ministry of Earth Sciences (MoES), Government of India. Other two simulations were performed with the MOZART-4 gas-phase chemical mechanism coupled with the Model for Simulating Aerosol Interactions and Chemistry (MOZART-MOSAIC), and Carbon Bond 5 (CB-05) gas-phase mechanism couple with the Modal Aerosol Dynamics Model for Europe/Secondary Organic Aerosol Model (CB05-MADE/SORGAM) aerosol mechanisms. The evaluation demonstrated that chemical mechanisms affect the evolution of gas-phase precursors and aerosol processes, which in turn affect the optical depth and overall performance of the model for PM2.5. All the three coupled schemes, MOZART-GOCART, MOZART-MOSAIC, and CB05-MADE/SORGAM, underestimate the observed concentrations of major aerosol composition (NO3−, SO42−, Cl−, BC, OC, and NH4+) and precursor gases (HNO3, NH3, SO2, NO2, and O3) over Delhi. Comparison with observations suggests that the simulations using MOZART-4 gas-phase chemical mechanism with MOSAIC aerosol performed better in simulating aerosols over Delhi and its optical depth over the IGP. The lowest NMB (−18.8 %, MB = −27.4 μg/m3) appeared for the simulations using MOZART-MOSAIC scheme, whereas the NMB was observed 32.5 % (MB = −47.5 μg/m3) for CB05-MADE/SORGAM and −53.3 % (MB = −78 μg/m3) for MOZART-GOCART scheme.

The occurrence of thick fog for longer duration in the northern regions of India disturbs the aviation, road transportation and other day to day activities. To understand the turbulence properties during fog period, we measured the atmospheric turbulent parameters along with carbon dioxide concentrations in the atmospheric boundary layer using eddy covariance system. These measurements were conducted over the agricultural station, Hisar, India, during the months of January–February of the year 2017 and 2018. During this period, total five thick fog events and three moderate fog events were captured. The turbulent parameter such as friction velocity, stability, sensible and latent heat fluxes are presented with respect to fog events. During the study period, the western disturbance persists over the north Pakistan and neighborhood region which advects the large amount of moisture into the lower troposphere and further through evaporation. It enforces stable and clear sky atmospheric conditions and reduces the surface temperature leading to the formation of strong surface-based temperature inversion which facilitates the fog formation in the study region. The land surface processes with neutral stability conditions in the surface layer, play significant role to sustain fog in the study region. The observations show substantial increase of carbon dioxide concentration during the thick fog events. The foggy days did not depict the diurnal pattern in flux of CO2. The anomalies of the meteorological parameters during foggy days and clear sky are analyzed. The foggy conditions (04:00–10:00 h, IST) are found to be characterized with low wind speed, high relative humidity with remarkable fluctuations in dew point temperature. Also, the sensible and latent heat flux shows remarkable changes during foggy and clear sky conditions.

We investigated the diurnal variation of tropospheric nitrogen dioxide (NO2) over a cluster of high emission thermal power plants (HETPPs) in India using two products from satellite-based UV/Visible spectrometers together with a chemistry-transport-model. The different overpass times of the satellite spectrometers: SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) (1030 h LT) and Ozone Monitoring Instrument (OMI) (1330 h LT)), enable detection of changes in NO2 columns at two different times of the day, providing some insight on its diurnal variation. Observations show elevated tropospheric NO2 columns in the afternoon compared to the morning in the vicinity of the HETPPs (similar to 65% higher), contrary to the expected decrease due to enhanced photochemical loss of NO2 in the afternoon and increased concentrations in the morning due to peak-hour vehicular emissions. The observed diurnal variability was simulated using a regional atmospheric composition model and the deviations in nitrogen oxide (NO), NO2, ozone (O-3) and hydroperoxy radical (HO2) between 1030 h and 1330 h within the planetary boundary layer (PBL) were examined. We also examined the vertical and horizontal accumulated tendencies of NO2 (advection) and wind profiles directly over the emission. The results show that the vertical variability of chemical loss processes, driven by a change in vertical mixing of NO2 within the PBL and variability in advection (driven by winds) leads to a net increase in the afternoon NO2 columns over the HETPPs, even if we assume flat emissions throughout the day. These results suggest that meteorology along with PBL evolution affects the diurnal evolution of NO2 columnar abundance over locations with high emissions in India.

Air quality has become one of the most important environmental concerns for Delhi, India. In this perspective, we have developed a high-resolution air quality prediction system for Delhi based on chemical data assimilation in the chemical transport model Weather Research and Forecasting with Chemistry (WRF-Chem). The data assimilation system was applied to improve the PM2.5 forecast via assimilation of MODIS aerosol optical depth retrievals using three-dimensional variational data analysis scheme. Near real-time MODIS fire count data were applied simultaneously to adjust the fire-emission inputs of chemical species before the assimilation cycle. Carbon monoxide (CO) emissions from biomass burning, anthropogenic emissions, and CO inflow from the domain boundaries were tagged to understand the contribution of local and non-local emission sources. We achieved significant improvements for surface PM2.5 forecast with joint adjustment of initial conditions and fire emissions.

Abstract. Elevated levels of fine particulate matter (PM2.5) during winter-time have become one of the most important environmental concerns over the Indo-Gangetic Plain (IGP) region of India, and particularly for Delhi. Accurate reconstruction of PM2.5, its optical properties, and dominant chemical components over this region is essential to evaluate the performance of the air quality models. In this study, we investigated the effect of three different aerosol mechanisms coupled with gas-phase chemical schemes on simulated PM2.5 mass concentration in Delhi using the Weather Research and Forecasting model with the Chemistry module (WRF-Chem). The model was employed to cover the entire northern region of India at 10 km horizontal spacing. Results were compared with comprehensive filed data set on dominant PM2.5 chemical compounds from the Winter Fog Experiment (WiFEX) at Delhi, and surface PM2.5 observations in Delhi (17 sites), Punjab (3 sites), Haryana (4 sites), Uttar Pradesh (7 sites) and Rajasthan (17 sites). The Model for Ozone and Related Chemical Tracers (MOZART-4) gas-phase chemical mechanism coupled with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) aerosol scheme (MOZART-GOCART) were selected in the first experiment as it is currently employed in the operational air quality forecasting system of Ministry of Earth Sciences (MoES), Government of India. Other two simulations were performed with the MOZART-4 gas-phase chemical mechanism coupled with the Model for Simulating Aerosol Interactions and Chemistry (MOZART-MOSAIC), and Carbon Bond 5 (CB-05) gas-phase mechanism couple with the Modal Aerosol Dynamics Model for Europe/Secondary Organic Aerosol Model (CB05-MADE/SORGAM) aerosol mechanisms. The evaluation demonstrated that chemical mechanisms affect the evolution of gas-phase precursors and aerosol processes, which in turn affect the optical depth and overall performance of the model for PM2.5. All the three coupled schemes, MOZART-GOCART, MOZART-MOSAIC, and CB05-MADE/SORGAM, underestimate the observed concentrations of major aerosol composition (NO3−, SO42−, Cl−, BC, OC, and NH4+) and precursor gases (HNO3, NH3, SO2, NO2, and O3) over Delhi. Comparison with observations suggests that the simulations using MOZART-4 gas-phase chemical mechanism with MOSAIC aerosol performed better in simulating aerosols over Delhi and its optical depth over the IGP. The lowest NMB (−18.8 %, MB = −27.4 μg/m3) appeared for the simulations using MOZART-MOSAIC scheme, whereas the NMB was observed 32.5 % (MB = −47.5 μg/m3) for CB05-MADE/SORGAM and −53.3 % (MB = −78 μg/m3) for MOZART-GOCART scheme.

A Winter Fog Experiment (WiFEX) was conducted to study the genesis of fog formation between winters 2016-17 and 2017-18 at Indira Gandhi International Airport (IGIA), Delhi, India. To support the WiFEX field campaign, the Weather Research and Forecasting (WRF) Model was used to produce real-time forecasts at 2-km horizontal grid spacing. This paper summarizes the performance of the model forecasts for 43 very dense fog episodes (visibility < 200 m) and preliminary evaluation of the model against the observations. Similarly, near-surface liquid water content (LWC) from models and continuous visibility observations are used as a metric for model evaluation. Results show that the skill score is relatively promising for the hit rate with a value of 0.78, whereas the false alarm rate (0.19) and missing rate (0.32) are quite low. This indicates that the model has reasonable predictive accuracy, and the performance of the real-time forecast is better for both dense fog events and no-fog events. For success cases, the model accurately captured the near-surface meteorological conditions, particularly the low-level moisture, wind fields, and temperature inversion. In contrast, for failed cases, the WRF Model shows large error in near-surface relative humidity and temperature compared to the observations, although it captures temperature inversions reasonably well. Our results also suggest that the model is able to capture the variability in fog onset for consecutive fog events. Errors in near-surface variables during failed cases are found to be affected by the errors in the initial conditions taken from the Indian Institute of Tropical Meteorology Global Forecasting System (IITM-GFS) spectral model forecast. Further evaluation of the operational forecasts for dense fog cases indicates that the error in predicting fog onset stage is relatively large (mean error of 4 h) compared to the dissipation stage.