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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 global-to-meso-scale dispersion model System for Integrated modeling for atmospheric composition (SILAM) has been implemented over the Indian region for operational air quality forecast. The regional SILAM model generates 96 hours forecasts over a domain at 3kmx3km horizontal resolution. The meteorological forcing is provided from the operational 3 km WRF model. The initial condition is derived from the forecast of the previous cycle of the regional SILAM model and the boundary condition is supplied from the global version of the model. Predicted mixing ratios of trace gases in the atmosphere are compared with the available ground-based observations over the North-Western Indian domain for two consecutive years (2021 and 2022). The paper presents a comprehensive evaluation of SILAM model performance against the observed surface data for trace gases (Surface O3, NO2, and CO). The spatial and temporal variability of trace gases over the domain is well simulated by the model. The model was also able to catch well the seasonality of trace gases (Surface Ozone, NO2, and CO) over the selected Indian domain. The forecast is found to be very skillful for trace gases and has been helping the air pollution control authorities in India to make informed decisions.

This paper discusses the newly developed Decision Support System version 1.0 (DSS v1.0) for air quality management activities in Delhi, India. In addition to standard air quality forecasts, DSS provides the contribution of Delhi, its surrounding districts, and stubble-burning fires in the neighboring states of Punjab and Haryana to the PM2.5 load in Delhi. DSS also quantifies the effects of local and neighborhood emission-source-level interventions on the pollution load in Delhi. The DSS-simulated Air Quality Index for the post-monsoon and winter seasons of 2021–22 shows high accuracy (up to 80 %) and a very low false alarm ratio (~20 %) from Day 1 to Day 5 of the forecasts, especially when the ambient AQI is > 300. During the post-monsoon season (winter season), emissions from Delhi, the rest of the NCR districts, biomass-burning activities, and all other remaining regions on average contribute 34.4 % (33.4 %), 31 % (40.2 %), 7.3 % (0.1 %), and 27.3 % (26.4 %), respectively, to PM2.5 load in Delhi. During peak pollution events (stubble-burning periods), however, the contribution from sources within Delhi (farm fires in Punjab-Haryana) could reach 65 % (69 %). According to DSS, a 20 % (40 %) reduction in anthropogenic emissions across all NCR districts would result in a 12 % (24 %) reduction in PM2.5 in Delhi on a seasonal mean basis. DSS is a critical tool for policymakers because it provides such information daily through a single simulation with a plethora of emission reduction scenarios.

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.

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.

Global urban population is projected to double by 2050. This rapid urbanization is the driver of economic growth but has environmental challenges. To that end, there is an urgent need to understand, simulate, and disseminate information about extreme events, routine city operations, and long-term planning decisions. This paper describes an effort underway in India involving an interdisciplinary community of meteorology, hydrology, air quality, and computer science from national and international institutes. The urban collaboratory is a system of systems for simulating weather, hydrology, air quality, health, energy, transport, and economy and its interactions. Study and prediction of urban events involve multiscale observations and cross-sector models, heterogeneous data management, and enormous computing power. The consortia program (NSM_Urban) is part of “weather ready cities,” under the aegis of India’s National Supercomputing Mission. The ecosystem “Urban Environment Science to Society” (UES2S) builds on the integrated cyberinfrastructure with a science gateway for community research and end-user service with modeling and interoperable data. The collaboratory has urban computing, stakeholder participation, and a coordinated means to scaffold projects and ideas into operational tools. It discusses the design and the utilization of high-performance computing (HPC) as a science cloud platform for bridging urban environment and data science, participatory stakeholder applications, and decision-making. The system currently integrates models for high-impact urban weather, flooding, air quality, and simulating street- and building-scale wind flow and dispersion. The program with the work underway is ripe for interfacing with regional and international partners, and this paper provides an avenue toward that end.

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 (

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.

This study reports a very high-resolution (400 m grid-spacing) operational air quality forecasting system developed to alert residents of Delhi and the National Capital Region (NCR) about forthcoming acute air pollution episodes. Such a high-resolution system has been developed for the first time and is evaluated during October 2019-February 2020. The system assimilates near real-time aerosol observations from in situ and space-borne platform in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to produce a 72-h forecast daily in a dynamical downscaling framework. The assimilation of aerosol optical depth and surface PM2.5 observations improves the initial condition for surface PM2.5 by about 45 µg/m3 (about 50%).The accuracy of the forecast degrades slightly with lead time as mean bias increase from + 2.5 µg/m3 on the first day to − 17 µg/m3 on the third day of forecast. Our forecast is found to be very skillful both for PM2.5 concentration and unhealthy/ very unhealthy air quality index categories, and has been helping the decision-makers in Delhi make informed decisions.

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.

To link short-term exposures of air pollutants to health outcomes, scientists must use high temporal and spatial resolution estimates of PM2.5 concentrations. In this work, we develop a daily PM2.5 product at 1 × 1 km2 spatial resolution across the eastern United States (east of 90° W) with the aid of 1 × 1 km2 MAIAC aerosol optical depth (AOD) data, 36 × 36 km2 WRF-Chem output, 1 × 1 km2 land-use type from the National Land Cover Database, and 0.125° × 0.125° ERA-Interim re-analysis meteorology. A gap-filling technique is applied to MAIAC AOD data to construct robust daily estimates of AOD when the satellite data are missing (e.g., areas obstructed by clouds or snow cover). The input data are incorporated into a multiple-linear regression model trained to surface observations of PM2.5 from the EPA Air Quality System (AQS) monitoring network. The model generates a high-fidelity estimate (r2 = 0.75 using a 10-fold random cross-validation) of daily PM2.5 throughout the eastern United States. Of the inputs to the statistical model, WRF-Chem output (r2 = 0.66) is the most important contributor to the skill of the model. MAIAC AOD is also a strong contributor (r2 = 0.52). Daily PM2.5 output from our statistical model can be easily integrated into county-level epidemiological studies. The novelty of this project is that we are able to simulate PM2.5 in a computationally efficient manner that is constrained to ground monitors, satellite data, and chemical transport model output at high spatial resolution (1 × 1 km2) without sacrificing the temporal resolution (daily) or spatial coverage (>2,000,000 km2).

The Indian subcontinent is a major hotspot of enhanced aerosol loading and emission of various chemical compounds due to extensive anthropogenic activities that profoundly impact the hydrological cycle of this region. Using coupled regional atmospheric chemistry transport model, WRF-Chem (Weather Research and Forecasting model coupled with chemistry) with fully interactive chemistry and dynamics, we carried out two sets of 122-days simulation experiments: (1) a control experiment without embedding chemistry and (2) a sensitivity experiment with embedding fully coupled MOZART-MOSAIC chemistry, to examine the impact of chemistry on the Indian Summer Monsoon Rainfall (ISMR) of 2017. The results show that inclusion of chemistry in the model plays a role in altering the spatial distribution of ISMR, reflected by a reduced mean rainfall by about 3 mm/day over the Indian subcontinent. The mean bias of model simulated rainfall reduces from 2.73 mm/day (excluding chemistry) to 1.6 mm/day (including chemistry), and from 1.2 mm/day (excluding chemistry) to 0.81 mm/day (including chemistry) when compared with IMD (India Meteorological Department) and TRMM (Tropical Rainfall Measuring Mission) rainfall data, respectively. Additionally, we found that inclusion of interactive chemistry causes a decrease in the daily convective rainfall by about 3 mm that is contributing largely to the mean rainfall differences. Further, analysis of hydrometeors indicates that the chemistry interacts with the cloud microphysics by modulating the hydrometeor paths of different cloud hydrometeors that consequently impacts the simulation of ISMR over this region. Results indicate that the virtue of detailed and more realistic representation of chemistry in the model has led to more realistic simulation and cannot be ignored in numerical forecasting of ISMR.

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.

Limited availability of atmospheric ammonia (NH3) observations, limits our understanding of controls on its spatial and temporal variability and its interactions with ecosystems. Here we used the Model for Ozone and Related chemical Tracers (MOZART-4) global chemistry transport model and the Hemispheric Transport of Air Pollution version-2 (HTAP-v2) emission inventory to simulate global NH3 distribution for the year 2010. We present a first comparison of the model with monthly averaged satellite distributions and limited ground-based observations available across South Asia. The MOZART-4 simulations over South Asia and East Asia are evaluated with the NH3 retrievals obtained from the Infrared Atmospheric Sounding Interferometer (IASI) satellite and 69 ground based monitoring stations for air quality across South Asia, and 32 ground based monitoring stations from the Nationwide Nitrogen Deposition Monitoring Network (NNDMN) of China. We identify the northern region of India (Indo-Gangetic Plain, IGP) as a hotspot for NH3 in Asia, both using the model and satellite observations. In general, a close agreement was found between yearly-averaged NH3 total columns simulated by the model and IASI satellite measurements over the IGP, South Asia (r = 0.85) and North China Plain (NCP), of East Asia (r = 0.88). However, the MOZART-4 simulated NH3 column is substantially higher over South Asia than East Asia, as compared with the IASI retrievals, which show smaller differences. Model simulated surface NH3 concentrations indicate smaller concentrations in all seasons than surface NH3 measured by the ground based observations over South and East Asia, although uncertainties remain in the available surface NH3 measurements. Overall, the comparison of East Asia and South Asia using both MOZART-4 model and satellite observations showed smaller NH3 columns in East Asia compared with South Asia for comparable emissions, indicating rapid dissipation of NH3 due to secondary aerosol formation, which can be explained by larger emissions of acidic precursor gases in East Asia.

The effect of relative humidity and temperature on the submicron aerosol variability and its ageing process was studied over a high altitude site, Mahabaleshwar in south-west India. The mass composition of non-refractory particulate matter of 1 μm (NR-PM1) size was obtained using Time of Flight Aerosol Chemical Speciation Monitor (ToF-ACSM) along with the measurements on a few trace gases during winter (December 2017–February 2018) and summer season (20th March - 5th May 2018). Sulfate exhibited strong dependence on the relative humidity (RH) as its mass fraction increased with the increase in RH. The Sulfate oxidation ratio (SOR) calculated during summer season also showed an increasing trend with RH indicating the influence of aqueous phase oxidation on sulfate fraction. On the other hand, OOA showed remarkable enhancement in its mass fraction with the increase in temperature along with the corresponding increase in f44 and tropospheric ozone. OOA, ozone and f44 ratio increased 14–34%, 8–26% and 25–43% respectively with the increase in temperature from 18 to 30 °C. This is indicative of the dominance of photochemical ageing processes during high temperature conditions. The extent of photochemical ageing was found to be higher during summer season (mean temperature ∼25.4 ± 2.6 °C) as compared to winter season (mean temperature ∼20.5 ± 2.6 °C). The nitrate diurnal was majorly governed by gas to particle partitioning process during winter season, whereas the summertime nitrate diurnal was influenced primarily by its formation rate. The non parametric wind regression analysis revealed that the mass concentration during winter was majorly contributed by distant sources from north east direction while during summer the local sources were more dominant.

Managing air quality levels in the National Capital Region (NCR), especially Delhi, India has emerged as a complicated task. It is now a matter of top priority to develop meaningful policy options. Short-term air quality forecasts can provide timely information about forthcoming air pollution episodes that the decision-makers can use to implement temporary emission control measures and reduce public exposure to extreme air pollution events. Although India has developed air quality forecasting systems for NCR, it was challenging to predict acute air pollution episodes during which hourly PM2.5 concentrations exceed 300 µg/m3. In this perspective, a very high-resolution (400 m) operational air quality prediction system has been developed to predict extreme air pollution events in Delhi and issue timely warnings. This modeling framework consists of a high-resolution fully coupled state-of-the-science Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and three-dimensional Variational (3DVAR) framework of the community Gridpoint Statistical Interpolation (GSI) system. The system assimilates satellite aerosol optical depth (AOD) retrievals at 10 km resolution, real-time crop residue burring at 1km resolution, surface PM2.5 data from 43 air quality monitoring stations, and uses high-resolution dynamical emissions (400 m) from various anthropogenic sources. The chemical data assimilation is further integrated with dynamical downscaling to obtain improved chemical conditions for the 400 m resolution domain. This paper summarizes the performance of the model forecasts for the winter season 2019-2020 and the evaluation of the model against the observations. Here, we demonstrate that the assimilation of chemical data in a coupled weather-air quality model improved the overall accuracy of PM2.5 forecasts in New Delhi by about 70 % during the winter season 2019-2020. Results show that the skill score for the poor (AQI 200-300), very-poor (AQI 300-400) and sever pollution (AQI 400-500) days is relatively promising for the hit rate with a value of 0.74 for (very-poor). This indicates that the model has reasonable predictive accuracy for air quality events. False Alarm rate (0.19), missing rate (0.32) are low, and the probability of detection is relatively high (0.67), indicating that the performance of the real-time forecast is better for both very poor events and no-very poor events.

Elevated PM2.5 concentrations frequently cause severe air pollution events in Delhi. Till recently, the effect of crop residue burning on the air quality in Delhi has not been fully quantified and the approaches to control the impact of fire emissions have not been effective. In this study, for the first time, we quantified the statewise contribution of post-monsoon crop residue burning in the northwestern states of India to surface PM2.5 concentrations in Delhi using several sensitivity experiments with the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and FINNv1.5 fire emission inventory. Results were evaluated with ground-based observations in Delhi (21 stations), Punjab, and Haryana (14 stations). On average, ∼20% of PM2.5 concentration in Delhi during the post-monsoon season (October-November) was found to be contributed by nonlocal fire emissions. However, on typical air pollution events, fire emissions contributed as high as 50-75% (80-120 μg/m3) to PM2.5 in Delhi, highlighting the importance of both external transport and local emissions to PM2.5 pollution in Delhi.

In this study, we used remotely sensed backscattered profiles from a ceilometer to characterize the vertical and horizontal mixing of aerosols in the polluted planetary boundary layer (PBL). These profiles revealed the structure of the boundary layer, which included the mixed layer, the nocturnal residual layer and the elevated aerosol layer far above the mixed layer over Delhi. The accumulation of aerosols near the surface during feeble turbulence and the mixing of aerosols from the residual layer into the surface layer during convection was captured very well by a ceilometer. The backscattered signal from a height of 45 m above the ground was strongly correlated (82%) with the observed surface PM2.5 and PM10 mass concentrations. We developed an empirical regression model based on this relationship, which was then tested and validated against independent measurements of the concentrations from November 2018. Although local meteorological conditions, particularly cloudiness and rain, influenced the strength of the correlation between the observed PM2.5 and PM10 mass concentrations and the backscattered signal, the magnitude of the mean bias between the observed and the values for PM2.5 (–21 µg m–3, RMSE = 75) and PM10 (31 µg m–3, RMSE = 118) indicated that the predicted values were fairly accurate. The model overestimated the PM2.5 by 7% and underestimated the PM10 by 6% on clear days.

The first measurements of cloud condensation nuclei (CCN) at five supersaturations were carried out onboard the research vessel “Sagar Kanya” (cruise SK-296) from the south to the head-bay of the Bay of Bengal as part of the Continental Tropical Convergence Zone (CTCZ) Project during the Indian summer monsoon of 2012. In this paper, we assess the diurnal variation in CCN distributions at supersaturations from 0.2% to 1% (in steps of 0.2%) and the power-law fit at supersaturation of 1%. The diurnal pattern shows peaks in CCN concentration (NCCN) at supersaturations from 0.2% to 1% between 0600 and 0700 LST (local standard time, UTC+0530), with relatively low concentrations between 1200 and 1400 LST, followed by a peak at around 1800 LST. The power-law fit for the CCN distribution at different supersaturation levels relates the empirical exponent (k) of supersaturation (%) and the NCCN at a supersaturation of 1%. The NCCN at a supersaturation of 0.4% is observed to vary from 702 cm−3 to 1289 cm−3, with a mean of 961±161 cm−3 (95% confidence interval), representing the CCN activity of marine air masses. Whereas, the mean NCCN of 1628±193 cm−3 at a supersaturation of 1% is higher than anticipated for the marine background. When the number of CCN spectra is 1293, the value of k is 0.57±0.03 (99% confidence interval) and its probability distribution shows cumulative counts significant at k ≈ 0.55±0.25. The results are found to be better at representing the features of the marine environment (103 cm−3 and k ≈ 0.5) and useful for validating CCN closure studies for Indian sea regions.

This paper presents a relationship of lightning activity with maximum air temperature, Bowen ratio, rainfall, cloud ice contents and Aerosol Optical Depth (AOD) over India during transition period from dry to wet seasons. Lightning flash count data and weather parameters are retrieved from the Tropical Rainfall Measuring Mission (Lightning Imaging Sensor - LIS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) satellites for the period of 17 years (1998–2014). The Pearson correlation between lightning flash count and Bowen ratio is found to be coefficient of R = 0.95 for both dry and wet seasons. For dry season, the Pearson correlation of lightning flash count with surface maximum air temperature is found to be coefficient of R = 0.97 whereas, that for wet season negative coefficient of R = −0.89. The comparative analysis of Pearson correlations of lightning flash counts with AOD, rainfall and cloud ice content are found to be coefficients higher by 20%, 28% and 34% for dry season than those of during wet season, respectively. The results of lightning activity and weather parameters in comparative analyses over the Indian region may be useful for better understanding the differential convection characteristics during transition period of dry to wet season. The results are also important for estimations of impact associated with lightning strikes to ground during dry and wet seasons.

Future ambient air quality will respond to changes in anthropogenic and biogenic emissions as well as climate changes, which may vary at national and state levels in different regions of the world. In this work, we applied an advanced online-coupled meteorology and chemistry model, the Weather Research and Forecasting Model with Chemistry (WRF/Chem), to the continental U.S. for current (2001-2010) and future (2046-2055) decades under four climate scenarios including the Representative Concentration Pathways (RCP) 4.5 and 8.5 and the Technology Driver Model (TDM) A1B and B2. Our goal is to quantify the impact of projected changes in anthropogenic and biogenic emissions and climate on future air quality under various climate scenarios for policy analysis for emission control and mitigation of adverse climate change. The simulations are performed at 36-, 12-km, and 4-km over North America, Continental U.S., and selected states, respectively. A comprehensive evaluation has been performed for the current simulation period using available observations from surface networks and satellites and shows an overall good performance in reproducing climatic and chemical observations at all grid scales. Future air quality features greater reduction in PM2.5 by RCP 4.5/8.5 than TDM B2/A1B and decreased O3 over most areas in the U.S. by RCP4.5 and TDM B2, indicating the benefits of carbon policy and technology changes with greater emission reductions. Air quality responds differently to projected changes in anthropogenic emissions in different states and seasons, indicating a need to develop state-specific emission control strategies for different seasons.

This study examines the contribution of 20% reduction in anthropogenic emissions from the energy, industry and transport sectors in South Asia to global distribution of ozone (O 3 ) during summer and winter monsoon seasons. We used Model for Ozone and Related chemical Tracers (MOZART-4) and Hemispheric Transport of Air Pollution version-2 (HTAP-v2) emission inventory to simulate global O 3 for five different sensitivity simulations. Contribution from different emission sectors is identified on the basis of the differences between model calculations with unperturbed emissions (Base-case) and the emissions reduced by 20% by different sectors over South Asia. During the summer season, 20% reduction in emissions from transportation sector contributes maximum decrease in O 3 of the order of 0.8 ppb in the center of Asian Summer Monsoons (ASM) anticyclone at 200 hPa. Response to Extra Regional Emission Reduction (RERER) is found to vary between 0.4 and 0.7 inside the ASM, indicating that 40–70% of O 3 trapped inside the anticyclone is influenced by the emission from non-Asian emissions, and the remaining O 3 is influenced by South-Asian emissions. During winter, 20% reduction in emissions from transport sector contributes decrease in O 3 at surface up to 0.5 ppb over South Asia and outflow region (the Arabian Sea and the Bay of Bengal). RERER values vary between 0 and 0.2 over South Asia indicating the predominant impact of local emissions reduction on surface O 3 concentration than reduction in foreign emissions. We have also examined the health benefits of reduction in regional, global and sectoral emissions in terms of decrease in excess number of COPD (Chronic Obstructive Pulmonary Disease) cases due to O 3 exposure. We find that more health benefits can be achieved if global emissions are decreased by 20%.

Air-sea interactions play an important role in atmospheric circulation and boundary layer conditions through changing convection processes and surface heat fluxes, particularly in coastal areas. These changes can affect the concentrations, distributions, and lifetimes of atmospheric pollutants. In this Part II paper, the performance of the Weather Research and Forecasting model with chemistry (WRF/Chem) and the coupled WRF/Chem with the Regional Ocean Model System (ROMS) (WRF/Chem-ROMS) are intercompared for their applications over quadruple-nested domains in Australia during the three following field campaigns: The Sydney Particle Study Stages 1 and 2 (SPS1 and SPS2) and the Measurements of Urban, Marine, and Biogenic Air (MUMBA). The results are used to evaluate the impact of air-sea interaction representation in WRF/Chem-ROMS on model predictions. At 3, 9, and 27 km resolutions, compared to WRF/Chem, the explicit air-sea interactions in WRF/Chem-ROMS lead to substantial improvements in simulated sea-surface temperature (SST), latent heat fluxes (LHF), and sensible heat fluxes (SHF) over the ocean, in terms of statistics and spatial distributions, during all three field campaigns. The use of finer grid resolutions (3 or 9 km) effectively reduces the biases in these variables during SPS1 and SPS2 by WRF/Chem-ROMS, whereas it further increases these biases for WRF/Chem during all field campaigns. The large differences in SST, LHF, and SHF between the two models lead to different radiative, cloud, meteorological, and chemical predictions. WRF/Chem-ROMS generally performs better in terms of statistics and temporal variations for temperature and relative humidity at 2 m, wind speed and direction at 10 m, and precipitation. The percentage differences in simulated surface concentrations between the two models are mostly in the range of &plusmn, 10% for CO, OH, and O3, &plusmn, 25% for HCHO, &plusmn, 30% for NO2, &plusmn, 35% for H2O2, &plusmn, 50% for SO2, &plusmn, 60% for isoprene and terpenes, &plusmn, 15% for PM2.5, and &plusmn, 12% for PM10. WRF/Chem-ROMS at 3 km resolution slightly improves the statistical performance of many surface and column concentrations. WRF/Chem simulations with satellite-constrained boundary conditions (BCONs) improve the spatial distributions and magnitudes of column CO for all field campaigns and slightly improve those of the column NO2 for SPS1 and SPS2, column HCHO for SPS1 and MUMBA, and column O3 for SPS2 at 3 km over the Greater Sydney area. The satellite-constrained chemical BCONs reduce the model biases of surface CO, NO, and O3 predictions at 3 km for all field campaigns, surface PM2.5 predictions at 3 km for SPS1 and MUMBA, and surface PM10 predictions at all grid resolutions for all field campaigns. A more important role of chemical BCONs in the Southern Hemisphere, compared to that in the Northern Hemisphere reported in this work, indicates a crucial need in developing more realistic chemical BCONs for O3 in the relatively clean SH.

This bottom-up modeling study, supported by new population census 2011 data, simulates ozone (O3) and fine particulate matter (PM2.5) exposure on local to regional scales. It quantifies, present-day premature mortalities associated with the exposure to near-surface PM2.5 and O3 concentrations in India using a regional chemistry model. We estimate that PM2.5 exposure leads to about 570,000 (CI95: 320,000–730,000) premature mortalities in 2011. On a national scale, our estimate of mortality by chronic obstructive pulmonary disease (COPD) due to O3 exposure is about 12,000 people. The Indo-Gangetic region accounts for a large part (~42%) of the estimated mortalities. The associated lost life expectancy is calculated as 3.4 ± 1.1 years for all of India with highest values found for Delhi (6.3 ± 2.2 years). The economic cost of estimated premature mortalities associated with PM2.5 and O3 exposure is about 640 (350–800) billion USD in 2011, which is a factor of 10 higher than total expenditure on health by public and private expenditure.

This study presents the distribution of tropospheric ozone and related species for South Asia using the Model for Ozone and Related chemical Tracers (MOZART-4) and Hemispheric Transport of Air Pollution version-2 (HTAP-v2) emission inventory. The model present-day simulated ozone (O3), carbon monoxide (CO) and nitrogen dioxide (NO2) are evaluated against surface-based, balloon-borne and satellite-based (MOPITT and OMI) observations. The model systematically overestimates surface O3 mixing ratios (range of mean bias about: 1–30 ppbv) at different ground-based measurement sites in India. Comparison between simulated and observed vertical profiles of ozone shows a positive bias from the surface up to 600 hPa and a negative bias above 600 hPa. The simulated seasonal variation in surface CO mixing ratio is consistent with the surface observations, but has a negative bias of about 50–200 ppb which can be attributed to a large part to the coarse model resolution. In contrast to the surface evaluation, the model shows a positive bias of about 15–20 × 1017 molecules/cm2 over South Asia when compared to satellite derived CO columns from the MOPITT instrument. The model also overestimates OMI retrieved tropospheric column NO2 abundance by about 100–250 × 1013 molecules/cm2. A response to 20% reduction in all anthropogenic emissions over South Asia shows a decrease in the anuual mean O3 mixing ratios by about 3–12 ppb, CO by about 10–80 ppb and NOX by about 3–6 ppb at the surface level. During summer monsoon, O3 mixing ratios at 200 hPa show a decrease of about 6–12 ppb over South Asia and about 1–4 ppb over the remote northern hemispheric western Pacific region.

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 (~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 (O3) 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. Keywords: Air Quality, Tropspheric NO2, WRF-Chem

An advanced online-coupled meteorology-chemistry model, WRF/Chem-MADRID, has been applied for real-time air quality forecast (RT-AQF) in southeastern U.S. since 2009. In this work, the model is evaluated for fine particles (PM2.5) forecast for time periods since winter 2015-2016 and intercompared with previous forecasting periods. The results show consistently good performance for PM2.5, despite the moderate overpredictions in winter 2015-2016. Sensitivity simulations with updated emissions and satellite-constrained boundary conditions show improved skills for PM2.5 forecast for summer 2016 and winter 2016-2017 with NMBs within ±6% for PM2.5 against observations from AirNow. The model’s forecasting skills for air quality can be further enhanced by accurately representing pollutant emissions from extreme events such as wildfires.

In this study, for the first time, the influence of springtime (MAM) biomass burning in South Asia on regional ozone (O3) distribution has been evaluated using a regional chemical transport model (WRF-Chem) and the Fire Inventory from NCAR (FINNv1). Model results are compared with satellite retrievals of tropospheric column amounts of carbon monoxide (CO) from MOPITT and nitrogen dioxide (NO2) from OMI. With daily varying emissions, the model captures reasonably well the satellite-derived temporal variations in CO and NO2 (index of agreement (R) for CO is 0.83 and for NO2 is 0.76), indicating the effectiveness of the model in estimating the overall fire impact on a regional scale. Simulated tropospheric NO2 concentration shows better agreement with the magnitude of observed NO2 when FINNv1 NOx emissions are reduced by a factor of 2.2 over the model domain. A clear increase in CO and NO2 levels over Burma (35–60%), Central India (15–30%), the Indo-Gangetic (15–25%) region and the Bay of Bengal (15–40%) are simulated with fire emissions. The model results are also used to quantify the net O3 production from fires. Calculated O3 productions are up to 4 ppb h−1 over inland and up to 0.1 ppb h−1 over marine regions respectively. Our model-based analysis yields average enhancement ratios ΔO3/ΔCO of 0.12 ppbv/ppbv and a total O3 production of about 3.5 Tg from South Asia during the spring season. The findings demonstrate that the springtime fire emissions in South Asia have a noticeable impact on the O3 in this region.

A network of air quality and weather monitoring stations was set-up across Delhi, India, under the System of Air quality Forecasting And Research (SAFAR) project. The objective of this network was to enable better understanding of air quality in terms of atmospheric chemistry, emissions and forecasting in Delhi, one of the largest metropolises in the world. In this study, we focus on the O3–NO–NO2-triad Photo Stationary State (PSS), and investigate site-specific deviations in the Leighton Ratio (Φ) during a short period in 2012 (1–31 December). Large variations were observed in the NO ( 1) were also observed occasionally, and these data were used to estimate the total peroxy radical (PO2) mixing ratios. This is the first estimate of PO2 reported for the city of Delhi and compares well with the results in the literature.