Performance of high resolution (400 m) PM2.5 forecast over Delhi

Effectiveness of respiratory face masks in reducing acute PM2.5 pollution exposure during peak pollution period in Delhi

The cities of North India, such as Delhi, face a significant public health threat from severe air pollution. Between October 2021 and January 2022, 79 % of Delhi's daily average PM2.5 (Particulate matter with an aerodynamic diameter ≤ 2.5 μm) values exceeded 100 μg/m3 (the permissible level being 60 μg/m3 as per Indian standards). In response to this acute exposure, using Respiratory Face Masks (RFMs) is a cost-effective solution to reduce immediate health risks while policymakers develop long-term emission control plans. Our research focuses on the health and economic benefits of using RFMs to prevent acute exposure to PM2.5 pollution in Delhi for different age groups. Our findings indicate that, among the fifty chosen RFMs, M50 has greatest potential to prevent short-term excess mortality (908 in age ranges 5-44), followed by M49 (745) and M48 (568). These RFMs resulted in estimated economic benefits of 500.6 (46 %), 411.1 (37 %), and 313.4 (29 %) million Indian Rupee (INR), respectively during October-January 2021-22. By wearing RFMs such as M50, M49, and M48 during episodes of bad air quality, it is estimated that 13 % of short-term excess mortality and associated costs could be saved if at least 30 % of Delhi residents followed an alert issued by an operational Air Quality Early Warning System (AQEWS) developed by the Ministry of Earth Sciences. Our research suggests that RFMs can notably decrease health and economic burdens amid peak PM2.5 pollution in post-monsoon and winter seasons until long-term emission reduction strategies are adopted. It is suggested that an advisory may be crafted in collaboration with statutory bodies and should be disseminated to assist the vulnerable population in using RFMs during winter. The analysis presented in this research is purely science based and outcomes of study are in no way to be construed as endorsement of product.

Determining hotspots of gaseous criteria air pollutants in Delhi airshed and its association with stubble burning

Transboundary pollutant transport is considered as one of the primary factors causing the seasonal air quality deterioration in Delhi, India's capital. The highest standard deviations exceeding days in winter for NO2 (7.14-9.63%) and SO2 (4.04-7.42%) in 2019-2022 underscore the role of meteorological conditions in Delhi's pollution. In contrast, the post-monsoon season shows the highest pollutant exceedance days (4.52-8.00%) for CO due to stubble burning (SB) in Punjab (68,902 fires/year). Despite the government's assertions of decreasing SB events (14.68%), the city's CO exceedance days persistently rose by 6.36%. CAMS data is used for assessing contribution hotspots through back-trajectory analysis at multiple heights. An overlap hotspot of 111 sq. km area is identified in the Southeast parts of Punjab that have a higher contribution to the CO levels in Delhi during the post-monsoon season of 2019. Similarly, hotspots are also observed for SO2 over industrial areas of Punjab during the post-monsoon and pre-monsoon seasons. The same seasons show similar contributing patterns for NO2 highlighting the influence of consistent emission patterns and meteorological conditions. The clear delineation of hotspots using the receptor model at multiple heights coupled with source apportionment studies will assist decision-makers in addressing the pollution sources outside Delhi.

Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm

Fine particulate matter (PM2.5) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In such a context, accurate prediction of PM2.5 concentration is critical for raising public awareness, allowing sensitive populations to plan ahead, and providing governments with information for public health alerts. This study applies a novel hybridization of extreme learning machine (ELM) with a snake optimization algorithm called the ELM-SO model to forecast PM2.5 concentrations. The model has been developed on air quality inputs and meteorological parameters. Furthermore, the ELM-SO hybrid model is compared with individual machine learning models, such as Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, and a deep learning model known as Long Short-Term Memory networks (LSTM), in forecasting PM2.5 concentrations. The study results suggested that ELM-SO exhibited the highest level of predictive performance among the five models, with a testing value of squared correlation coefficient (R2) of 0.928, and root mean square error of 30.325 µg/m3. The study's findings suggest that the ELM-SO technique is a valuable tool for accurately forecasting PM2.5 concentrations and could help advance the field of air quality forecasting. By developing state-of-the-art air pollution prediction models that incorporate ELM-SO, it may be possible to understand better and anticipate the effects of air pollution on human health and the environment.

Thermodynamical framework for effective mitigation of high aerosol loading in the Indo-Gangetic Plain during winter

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.

Stubble-burning activities in north-western India in 2021: Contribution to air pollution in Delhi

Stubble-burning in northern India is an important source of atmospheric particulate matter (PM) and trace gases, which significantly impact local and regional climate, in addition to causing severe health risks. Scientific research on assessing the impact of these burnings on the air quality over Delhi is still relatively sparse. The present study analyzes the satellite-retrieved stubble-burning activities in the year 2021, using the MODIS active fire count data for Punjab and Haryana, and assesses the contribution of CO and PM_2.5 from such biomass-burning activities to the pollution load in Delhi. The analysis suggests that the satellite-retrieved fire counts in Punjab and Haryana were the highest among the last five years (2016-2021). Further, we note that the stubble-burning fires in the year 2021 are delayed by ∼1 week compared to that in the year 2016. To quantify the contribution of the fires to the air pollution in Delhi, we use tagged tracers for CO and PM_2.5 emissions from fire emissions in the regional air quality forecasting system. The modeling framework suggests a maximum daily mean contribution of the stubble-burning fires to the air pollution in Delhi in the months of October-November 2021 to be around 30-35%. We find that the contribution from stubble burning activities to the air quality in Delhi is maximum (minimum) during the turbulent hours of late morning to afternoon (calmer hours of evening to early morning). The quantification of this contribution is critical from the crop-residue and air-quality management perspective for policymakers in the source and the receptors regions, respectively.

Sensitivity of WRF/Chem simulated PM2.5 to initial/boundary conditions and planetary boundary layer parameterization schemes over the Indo-Gangetic Plain

The ability of a chemical transport model to simulate accurate meteorological and chemical processes depends upon the physical parametrizations and quality of meteorological input data such as initial/boundary conditions. In this study, weather research and forecasting model coupled with chemistry (WRF-Chem) is used to test the sensitivity of PM_2.5 predictions to planetary boundary layer (PBL) parameterization schemes (YSU, MYJ, MYNN, ACM2, and Boulac) and meteorological initial/boundary conditions (FNL, ERA-Interim, GDAS, and NCMRWF) over Indo-Gangetic Plain (Delhi, Punjab, Haryana, Uttar Pradesh, and Rajasthan) during the winter period (December 2017 to January 2018). The aim is to select the model configuration for simulating PM_2.5 which shows the lowest errors and best agreement with the observed data. The best results were achieved with initial/boundary conditions from ERA and GDAS datasets and local PBL parameterization (MYJ and MYNN). It was also found that PM_2.5 concentrations are relatively less sensitive to changes in initial/boundary conditions but in contrast show a stronger sensitivity to changes in the PBL scheme. Moreover, the sensitivity of the simulated PM_2.5 to the choice of PBL scheme is more during the polluted hours of the day (evening to early morning), while that to the choice of the meteorological input data is more uniform and subdued over the day. This work indicates the optimal model setup in terms of choice of initial/boundary conditions datasets and PBL parameterization schemes for future air quality simulations. It also highlights the importance of the choice of PBL scheme over the choice of meteorological data set to the simulated PM_2.5 by a chemical transport model.

Four year long simulation of carbonaceous aerosols in India: Seasonality, sources and associated health effects

India's air quality is in a dismal state, with many studies ascribing it to PM_2.5. Most of these corroborate that carbonaceous aerosol (CA) constitute significant fraction of PM_2.5. However, investigations on the effect of long-term meteorological or emission changes on PM_2.5 and its components, and their associated health effects are rare. In this work, WRF-Chem simulations for three seasons over four years (2016-2019) were carried out to cogitate the spatial and temporal changes in PM_2.5 and its components in India. Model predicted PM_2.5 concentrations were in good agreement with the ground-based observations for 25 cities. PM_2.5 was highest in winter and lowest in pre-monsoon. PM_2.5 reduced by ∼8% in Indo-Gangetic Plain (IGP) but increased by ∼38% and ∼130% in south and northeast India, respectively, from 2016 to 2019. IGP witnessed three times higher average PM_2.5 concentrations than south India. No significant interannual change in CA contributions was observed, however, it peaked in the winter season. Other inorganics (OIN) were the major component of PM_2.5, contributing more than 40%. Primary organic aerosol (POA) fractions were higher in north India, while secondary inorganic aerosol (SIA) dominated south India. Transport and residential sectors were the chief contributors to CA across India. Biomass burning contributed up to ∼23% of PM_2.5 in regions of IGP during post-monsoon, with CA fractions up to 50%. Associations between PM_2.5 and its components with daily inpatient admissions from a tertiary care centre in Delhi showed that PM_2.5 and OIN had lower associations with daily hospital admissions than CA. Every 10 μg/m³ increase in POA, black carbon (BC), and secondary organic aerosol (SOA) were associated with ∼1.09%, ∼3.07% and ∼4.93% increase in the risk of daily hospital admissions. This invigorates the need for more policies targeting CA rather than PM_2.5 to mitigate associated health risks, in India.

Effect of COVID-19-induced lockdown on NO2 pollution using TROPOMI and ground-based CPCB observations in Delhi NCR, India

The present study investigates the reduction in nitrogen dioxide (NO2) levels using satellite-based (Sentinel-5P TROPOMI) and ground-based (Central Pollution Control Board) observations of 2020. The lockdown duration, monthly, seasonal and annual changes in NO2 were assessed comparing the similar time period in 2019. The study also examines the role of atmospheric parameters like wind speed, air temperature, relative humidity, solar radiation and atmospheric pressure in altering the monthly and annual values of the pollutant. It was ascertained that there was a mean reduction of ~ 61% (~ 66.5%), ~ 58% (~ 51%) in daily mean NO2 pollution during lockdown phase 1 when compared with similar period of 2019 and pre-lockdown phase in 2020 from ground-based (satellite-based) measurements. April month with ~ 57% (~ 57%), summer season with ~ 48% (~ 32%) decline and an annual reduction of ~ 20% (~ 18%) in tropospheric NO2 values were observed (p < 0.001) compared to similar time periods of 2019. It was assessed that the meteorological parameters remained almost similar during various parts of the year in 2019 and 2020, indicating a negligent role in reducing the values of atmospheric pollution, particularly NO2 in the study area. It was concluded that the halt in anthropogenic activities and associated factors was mainly responsible for the reduced values in the Delhi conglomerate. Similar work can be proposed for other pollutants to holistically describe the pollution scenario as an aftermath of COVID-19-induced lockdown.

Low-cost nature-inspired deep learning system for PM2.5 forecast over Delhi, India

Air quality has a tremendous impact on India's health and prosperity. Air quality models are crucial tools for surveying and projecting air pollution episodes, which can be used to issue health advisories to take action ahead of time. Short-term increases in air pollution trigger many adverse health events; a fast, efficient, cost-effective, and reliable air quality prediction model would aid in minimizing the effect on health and prosperity. Deterministic models, on the other hand, are less robust in predicting the pollutant series since it is non-stationary and non-linear. Atmospheric chemistry models are computationally expensive and often rely on outdated emissions information. We propose a deep learning model in this study that integrates neural networks, fuzzy inference systems, and wavelet transforms to predict the most prominent air pollutant affecting Delhi, India i.e., PM2.5 (particulate matter of aerodynamic diameter less than or equal to 2.5 µm). We have included the main aspects of air quality models in this research i.e., less computational time (7 min approximately using I5-1035G1, 1.19 GHz processor), less resource-intensive (dependent only on the pollutant lagged values), and high spatial resolution (1 km) for forecasting air quality three days ahead. The model predictions show a significant correlation coefficient lying in [0.96,0.98], [0.86,0.93], and [0.82,0.91] with Central Pollution Control Board (CPCB) monitored data at various sites in Delhi for one, two, and three days of forecast respectively.

Enhanced secondary aerosol formation driven by excess ammonia during fog episodes in Delhi, India

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 PM_2.5) 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^-, SO₄^2-, 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 PM_2.5 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 μm. The sulfur oxidation ratio (SOR) of PM₁, PM_2.5 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.

Effects of Air Pollutants on Summer Precipitation in Different Regions of Beijing

Many studies have shown that air pollutants have complex impacts on urban precipitation. Meteorological weather station and satellite Aerosol Optical Depth (AOD) product data from the last 20 years, combined with simulation results from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), this paper focuses on the effects of air pollutants on summer precipitation in different regions of Beijing. These results showed that air pollution intensity during the summer affected the precipitation contribution rate (PCR) of plains and mountainous regions in the Beijing area, especially in the plains. Over the past 20 years, plains PCR increased by ~10% when the AOD augmented by 0.15, whereas it decreased with lower pollution levels. In contrast, PCR in mountainous areas decreased with higher pollution levels and increased with lower pollution levels. Our analysis from model results indicated that aerosol increases reduce the effective particle size of cloud droplets and raindrops. Smaller cloud raindrops more readily transport to high air layers and participate in the generation of ice-phase substances in the clouds, increasing the total amount of cloud water in the air in a certain time, which ultimately enhanced precipitation intensity on the plains. The removal of pollutants caused by increased precipitation in the plains decreased rainfall levels in mountainous areas.

Probing wintertime air pollution sources in the Indo-Gangetic Plain through 52 hydrocarbons measured rarely at Delhi & Mohali

During wintertime, the Indo-Gangetic Plain suffers from severe air pollution affecting several hundred million people. Here we present unprecedented measurements and source analyses of 52 NMHCs (25 alkanes, 16 aromatics, 10 alkenes and one alkyne) in the cities of Delhi and Mohali (300 km north of Delhi) during wintertime (Dec 2016-Jan 2017). NMHCs were measured using a thermal desorption gas chromatograph equipped with flame ionisation detectors with data traceable to WMO standards. The ten most abundant NMHCs that were measured were the same at both Delhi and Mohali: propane, n-butane, acetylene, ethane, toluene, i-butane, ethene, i-pentane, benzene and propene and accounted for >50% of total measured NMHC mass concentration (137 ± 5.8 μg m^-3 in Mohali and 239 ± 7.7 μg m^-3 in Delhi). Ambient NMHCs and calculated hydroxyl radical reactivity were approximately twice as high in Delhi relative to Mohali, and 2-12 times higher than most other mega-cities, except Lahore and Karachi. Using chemical source signatures, traffic and LPG usage emissions were identified as the major contributor of these reactive NMHCs at both sites during nighttime, with additional minor contributions of garbage burning in Mohali, and evaporative fuel and biomass burning emissions in Delhi. Comparison of NMHC/CO and NMHC/C₂H₂ ratios over Mohali and Delhi, to other cities, suggested gasoline/petrol-fuelled vehicles were major NMHC emitters within the traffic source. The data from both Mohali and Delhi suggest that a large fraction of the fleet comprised vehicles with older emission control in both Mohali and Delhi. Analyses revealed poor representation of propene, ethene and trimethylbenzenes in the emission inventory (EDGARv4.3.2) over Mohali and Delhi. This study provides key data and new insights into the sources of reactive NMHCs (lifetime < few days) that drive regional wintertime pollution through direct effects and the formation of secondary pollutants.