A simple and rapid method for the highly sensitive determination of polycyclic aromatic hydrocarbons (PAHs) from airborne fine particulate matter (PM2.5) in an urban environment of Delhi was developed. The target compounds were 10 of the 16 United States Environmental Protection Agency (US-EPA) priority PAHs: fluoranthene, pyrene, chrysene, benzo(a)anthracene, benzo(b)fluoranthene, benzo(k)fluoranthene, benzo[a]pyrene, dibenzo(ah)anthracene, benzo(ghi)perylene, indeno(1,2,3-cd)pyrene. For collecting the samples, the following two locations in Delhi (India) were chosen: ITO and Okhla Industrial Area.  Two sets of samples at these locations of were collected for the purpose of investigation. The fine particulate matter samples were collected on glass fiber filter papers for 24h, from which the PAHs were extracted using dichloromethane (DCM) and hexane using ultrasonication method. Comparison of the characteristic emission of spectra of PAHs with standard spectra indicated the degree of condensation of aromatic compounds present in the investigated mixtures. However, this identification could be more effective with the use of the respective values of Δλ parameter for each particular component of the mixture. It has been found that the concentration of the PAHs is maximum during the winter season and minimum during the summer and monsoon seasons at both the locations.

The impacts of COVID-19 lockdown restrictions have provided a valuable global experiment into the extent of improvements in air quality possible with reductions in vehicle movements. Mexico City, London and Delhi all share the problem of air quality failing WHO guideline limits, each with unique situations and influencing factors. We determine, discuss and compare the air quality changes across these cities during the COVID-19, to understand how the findings may support future improvements in their air quality and associated health of citizens. We analysed ground-level PM₁₀, PM₂.₅, NO₂, O₃ and CO changes in each city for the period 1st January to August 31, 2020 under different phases of lockdown, with respect to daily average concentrations over the same period for 2017 to 2019. We found major reductions in PM₁₀, PM₂.₅, NO₂ and CO across the three cities for the lockdown phases and increases in O₃ in London and Mexico City but not Delhi. The differences were due to the O₃ production criteria across the cities, for Delhi production depends on the VOC-limited photochemical regime. Levels of reductions were commensurate with the degree of lockdown. In Mexico City, the greatest reduction in measured concentration was in CO in the initial lockdown phase (40%), in London the greatest decrease was for NO₂ in the later part of the lockdown (49%), and in Delhi the greatest decrease was in PM₁₀, and PM₂.₅ in the initial lockdown phase (61% and 50%, respectively). Reduction in pollutant concentrations agreed with reductions in vehicle movements. In the initial lockdown phase vehicle movements reduced by up to 59% in Mexico City and 63% in London. The cities demonstrated a range of air quality changes in their differing geographical areas and land use types. Local meteorology and pollution events, such as forest fires, also impacted the results.

Lockdown measures to contain COVID-19 pandemic has resulted in a considerable change in air pollution worldwide. We estimate the temporal and diurnal changes of the six criteria air pollutants, including particulate matter (PM₂.₅ and PM₁₀) and gaseous pollutants (NO₂, O₃, CO, and SO₂) during lockdown (25ᵗʰ March – 3ʳᵈ May 2020) across regions of India using the observations from 134 real-time monitoring sites of Central Pollution Control Board (CPCB). Significant reduction in PM₂.₅, PM₁₀, NO₂, and CO has been found in all the regions during the lockdown. SO₂ showed mixed behavior, with a slight increase at some sites but a comparatively significant decrease at other locations. O₃ also showed a mixed variation with a mild increase in IGP and a decrease in the South. The absolute decrease in PM₂.₅, PM₁₀, and NO₂ was observed during peak morning traffic hours (08–10 Hrs) and late evening (20–24 Hrs), but the percentage reduction is almost constant throughout the day. A significant decrease in day-time O₃ has been found over Indo Gangetic plain (IGP) and central India, whereas night-time O₃ has increased over IGP due to less O₃ loss. The most significant reduction (∼40–60%) was found in PM₂.₅ and PM₁₀. The highest decrease in PM was found for the north-west and IGP followed by South and central regions. A considerable reduction (∼30–70%) in NO₂ was found except for a few sites in the central region. A similar pattern was observed for CO having a ∼20–40% reduction. The reduction observed for PM₂.₅, PM₁₀, NO₂, and enhancement in O₃ was proportional to the population density. Delhi’s air quality has improved with a significant reduction in primary pollutants, however, an increase in O₃ was observed. The changes reported during the lockdown are combined effect of changes in the emissions, meteorology, and atmospheric chemistry that requires detailed investigations.

In recent years, severe pollution events were observed frequently in India especially at its capital, New Delhi. However, limited studies have been conducted to understand the sources to high pollutant concentrations for designing effective control strategies. In this work, source-oriented versions of the Community Multi-scale Air Quality (CMAQ) model with Emissions Database for Global Atmospheric Research (EDGAR) were applied to quantify the contributions of eight source types (energy, industry, residential, on-road, off-road, agriculture, open burning and dust) to fine particulate matter (PM2.5) and its components including primary PM (PPM) and secondary inorganic aerosol (SIA) i.e. sulfate, nitrate and ammonium ions, in Delhi and three surrounding cities, Chandigarh, Lucknow and Jaipur in 2015. PPM mass is dominated by industry and residential activities (>60%). Energy (∼39%) and industry (∼45%) sectors contribute significantly to PPM at south of Delhi, which reach a maximum of 200 μg/m³ during winter. Unlike PPM, SIA concentrations from different sources are more heterogeneous. High SIA concentrations (∼25 μg/m³) at south Delhi and central Uttar Pradesh were mainly attributed to energy, industry and residential sectors. Agriculture is more important for SIA than PPM and contributions of on-road and open burning to SIA are also higher than to PPM. Residential sector contributes highest to total PM2.5 (∼80 μg/m³), followed by industry (∼70 μg/m³) in North India. Energy and agriculture contribute ∼25 μg/m³ and ∼16 μg/m³ to total PM2.5, while SOA contributes <5 μg/m³. In Delhi, industry and residential activities contribute to 80% of total PM2.5.

Excess nitrogen deposition is a matter of concern for sensitive ecosystems. However, understanding the sources and transport of Nr species has been a challenge due to limited observations of atmospheric deposition of the key Nr species across India. In this study, wet deposition of atmospheric inorganic Nr species was investigated during the year 2013 at two regionally representative sites: Delhi (an urban site) and Jaunpur (a rural site). These sites are located in the Indo-Gangetic Plain (IGP) region, which is one of the most populated and fertile regions of India. The average NH4+ concentrations in rain water were found to be 25.4 μeql−1 and 98.5 μeql−1 at the rural and urban sites, respectively, whereas average NO3− concentrations were 12.4 μeql−1 and 28.7 μeql−1 at the rural and urban sites (respectively). The annual average wet deposition fluxes of NH4+ and NO3− at Delhi were calculated as 10.45 and 3.05 kgN ha−1 yr−1 respectively, whereas at Jaunpur the fluxes were 3.19 and 1.56 kgN ha−1 yr−1 respectively. In order to assess the Nr deposition, our estimates showed 486% increase in NO3− (from 0.52 to 3.05 kgN ha−1 yr−1) while 283% NH4+ (2.72–10.44 kgN ha−1 yr−1) between 1994 and 2013 at Delhi, clearly indicating the effect of urbanization and Land Use Land Cover (LULC) change. Reduced versus oxidized N deposition contribution was also estimated. This study provides key quantitative information to support regional nitrogen budget estimates in south Asia.