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Countries around the world introduced strict restrictions on movement and activities known as 'lockdowns' to restrict the spread of the novel coronavirus disease (COVID-19) from the end of 2019. A sudden improvement in air quality was observed globally as a result of these lockdowns. To provide insight into the changes in air pollution levels in response to the COVID-19 restrictions we have compared surface air quality data in Delhi during four phases of lockdown and the first phase of the restriction easing period (25 March to 30 June 2020) with data from a baseline period (2018–2019). Simultaneously, short-term exposure of PM2.5 and O3 attributed premature mortality were calculated to understand the health benefit of the change in air quality. Ground–level observations in Delhi showed that concentrations of PM10, PM2.5 and NO2 dropped substantially in 2020 during the overall study period compared with the same period in previous years, with average reductions of ~49%, ~39%, and ~39%, respectively. An overall lower reduction in O3 of ~19% was observed for Delhi. A slight increase in O3 was found in Delhi's industrial and traffic regions. The highest peak of the diurnal variation decreased substantially for all the pollutants at every phase. The decrease in PM2.5 and O3 concentrations in 2020, prevented 904 total premature deaths, a 60% improvement when compared to the figures for 2018–2019. The restrictions on human activities during the lockdown have reduced anthropogenic emissions and subsequently improved air quality and human health in one of the most polluted cities in the world. Implications: I am submitting herewith the manuscript entitled "Unprecedented Reduction in Air Pollution and Corresponding Short-term Premature Mortality Associated with COVID-19 Forced Confinement in Delhi, India" for potential publishing in your journal. The novelty of this research lies in: (1) we utilized ground-level air quality data in Delhi during four phases of lockdown and the first phase of unlocking period (25th March to 30th June) for 2020 as well as data from the baseline period (2018–2019) to provide an early insight into the changes in air pollution levels in response to the COVID-19 pandemic, (2) Chatarize the change of diurnal variation of the pollutants and (3) we assess the health risk due to PM2.5 and O3. Results from ground-level observations in Delhi showed that concentrations of PM10, PM2.5 and NO2 substantially dropped in 2020 during the overall study period compared to the similar period in previous years, with an average reduction of ~49%, ~39%, and ~39%, respectively. In the case of O3, the overall reduction was observed as ~19% in Delhi, while a slight increase was found in industrial and traffic regions. And consequently, the highest peak of the diurnal variation decreased substantially for all the pollutants. The health impact assessment of the changes in air quality indicated that 904 short-term premature deaths (~60%) were prevented due to the decline in PM2.5 and O3 concentrations in the study period. The restrictions on human activities during the lockdown have reduced the anthropogenic emissions and subsequently improved air quality and human health in one of the most polluted cities in the world. [ABSTRACT FROM AUTHOR]

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 PM 10 , PM 2.5 , NO 2 , O 3 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 PM 10 , PM 2.5 , NO 2 and CO across the three cities for the lockdown phases and increases in O 3 in London and Mexico City but not Delhi. The differences were due to the O 3 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 NO 2 in the later part of the lockdown (49%), and in Delhi the greatest decrease was in PM 10 , and PM 2.5 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. [Display omitted] • COVID-19 restrictions reduced traffic and improved air quality in urban areas. • We report the changes in PM 2.5 , NO 2 , O 3 and CO in Mexico City, London and Delhi. • CO and NO 2 concentrations across measurement sites were down by 48%. • PM 2.5 was influenced by forest fires, seasonal crop burning and weather conditions. • O 3 increased across Mexico City and London, with a mix of effects within Delhi. [ABSTRACT FROM AUTHOR]

• The spatiotemporal characteristics of PM 10 , PM 2.5 and gaseous pollutants are examined. • High degree of homogeneity was observed for PM 2.5 concentrations across all sites in Delhi. • High PM and gaseous pollutants (except O 3) concentrations were observed during winter. • Local sources dominate PM concentrations at most sites across Delhi and the NCR region. • Currently configured network does not permit evaluation of long-range transport between Delhi and the NCR (and vice-versa). A key challenge in controlling Delhi's air quality is a lack of clear understanding of the impacts of emissions from the surrounding National Capital Region (NCR). Our objectives are to understand the limitations of publicly available data, its utility to determine pollution sources across Delhi-NCR and establish seasonal profiles of chemically active trace gases. We obtained the spatiotemporal characteristics of daily-averaged particulate matter (PM 10 and PM 2.5) and trace gases (NO X , O 3 , SO 2 , and CO) within a network of 12 air quality monitoring stations located over 2000 km2 across Delhi-NCR from January 2014 to December 2017. The highest concentrations of pollutants, except O 3 , were found at Anand Vihar compared with lowest at Panchkula. A high homogeneity in PM 2.5 was observed among Delhi sites as opposed to a high spatial divergence between Delhi and NCR sites. The bivariate polar plots and k-means clustering showed that PM 2.5 and PM 10 concentrations are dominated by local sources for all monitoring sites across Delhi-NCR. A consequence of the dominance of local source contributions to measured concentrations, except to one site remote from Delhi, is that it is not possible to evaluate the influence of regional pollution transport upon PM concentrations measured at sites within Delhi and the NCR from concentration measurements alone. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic perturbed air pollutant emissions as cities shut down worldwide. Peroxyacyl nitrates (PANs) are important tracers of photochemistry that are formed through the oxidation of non‐methane volatile organic compounds in the presence of nitrogen oxide radicals (NOx = NO + NO2). We use satellite measurements of free tropospheric PANs from the Suomi‐National Polar‐orbiting Partnership Cross‐track Infrared Sounder (CrIS) over eight of the world's megacities. We quantify the seasonal cycle of PANs over these megacities and find seasonal maxima in PANs correspond to seasonal peaks in local photochemistry. CrIS is used to explore changes in PANs in response to the COVID‐19 lockdowns. Statistically significant changes to PANs occurred over four megacities: with decreases over Los Angeles and Delhi, and increases over Mexico City and Beijing in the winter. Our analysis suggests that large perturbations in NOx may not result in significant declines in NOx export potential of megacities. Plain Language Summary: The COVID‐19 pandemic led to the lockdown of urban centers worldwide, drastically perturbing the concentrations of global air pollutants. Peroxyacyl nitrates (PANs) are important photochemical pollutants formed from reactions between NOx and volatile organic compounds, which were substantially reduced during the pandemic. We use satellite measurements of PANs from the Suomi‐National Polar‐orbiting Partnership Cross‐track Infrared Sounder in the free troposphere over and surrounding eight of the world's megacities. Seasonal cycles of PANs are pronounced and the seasonal maxima correspond to seasonal peaks in local photochemistry. Significant changes to PANs in response to COVID‐19 occurred over four out of the eight cities: PANs decreased over Los Angeles and Delhi, and PANs increased over Mexico City and Beijing in the winter. Our results indicate that large changes in NOx may not result in equally significant changes to PANs and the NOx export potential of megacities. Key Points: There are pronounced seasonal cycles of peroxyacyl nitrates (PANs) over each megacity that align with seasonal maximums in photochemistryObserved free tropospheric mixing ratios of PANs during COVID‐19 were significantly different over four out of eight surveyed megacitiesSensitivity of free tropospheric PANs to the abundance of precursors is seasonally dependent in some locations [ABSTRACT FROM AUTHOR]

The inhalation of particulate matter (PM) has been linked to various respiratory illnesses, including asthma, chronic obstructive pulmonary disease, and cancer. The COVID-19 pandemic has heightened concerns about PM as a possible route of virus transmission through airborne particles. This study investigated the impact of lockdown on particulate matter and mass-segregated PM (PM2.5 and PM10) deposition in human lungs. In this study, the daily average concentration of particulate matter (PM2.5 and PM10) was observed from March 22 to May 15, 2021, and grouped into two categories: before and during lockdown. The lockdown resulted in a 1.6% decrease in PM2.5 concentration and a 15% decrease in PM10 concentration. Ground-level particulate matter concentrations were found to be positively correlated with daily new COVID-19 cases and daily deaths during the lockdown period. Meteorological temperature was found to be positively associated with daily cases and daily deaths due to COVID-19. The noncarcinogenic risk was calculated by hazard quotient (HQ) with annual daily dose (ADD), and it exceeded the target value of 1 in almost all cases. Analysis of airway geometry and mass deposition indicated that mass deposition was reduced by 25% and 13% for PM2.5 and PM10 , respectively. Therefore, ambient air pollution may be considered to be a major cofactor for the high death rate and number of daily cases during the pandemic. Based on the obtained results, the study recommends implementing effective pollution mitigation strategies to reduce the risk of respiratory illnesses and disease transmission through the air as a medium. [ABSTRACT FROM AUTHOR]

Exposure to air pollution is a leading public health risk factor in India, especially over densely populated Delhi and the surrounding Indo-Gangetic Plain. During the post-monsoon seasons, the prevailing north-westerly winds are known to influence aerosol pollution events in Delhi by advecting pollutants from agricultural fires as well as from local sources. Here we investigate the year-round impact of meteorology on gaseous nitrogen oxides (NOx=NO+NO2). We use bottom-up NOx emission inventories (anthropogenic and fire) and high-resolution satellite measurement based tropospheric column NO2 (TCNO 2) data, from S5P aboard TROPOMI, alongside a back-trajectory model (ROTRAJ) to investigate the balance of local and external sources influencing air pollution changes in Delhi, with a focus on different emissions sectors. Our analysis shows that accumulated emissions (i.e. integrated along the trajectory path, allowing for chemical loss) are highest under westerly, north-westerly and northerly flow during pre-monsoon (February–May) and post-monsoon (October–February) seasons. According to this analysis, during the pre-monsoon season, the highest accumulated satellite TCNO 2 trajectories come from the east and north-west of Delhi. TCNO 2 is elevated within Delhi and the Indo-Gangetic Plain (IGP) to the east of city. The accumulated NOx emission trajectories indicate that the transport and industry sectors together account for more than 80 % of the total accumulated emissions, which are dominated by local sources (>70 %) under easterly winds and north-westerly winds. The high accumulated emissions estimated during the pre-monsoon season under north-westerly wind directions are likely to be driven by high NOx emissions locally and in nearby regions (since NOx lifetime is reduced and the boundary layer is relatively deeper in this season). During the post-monsoon season the highest accumulated satellite TCNO 2 trajectories are advected from Punjab and Haryana, where satellite TCNO 2 is elevated, indicating the potential for the long-range transport of agricultural burning emissions to Delhi. However, accumulated NOx emissions indicate local (70 %) emissions from the transport sector are the largest contributor to the total accumulated emissions. High local emissions, coupled with a relatively long NOx atmospheric lifetime and shallow boundary layer, aid the build-up of emissions locally and along the trajectory path. This indicates the possibility that fire emissions datasets may not capture emissions from agricultural waste burning in the north-west sufficiently to accurately quantify their influence on Delhi air quality (AQ). Analysis of daily ground-based NO2 observations indicates that high-pollution episodes (>90 th percentile) occur predominantly in the post-monsoon season, and more than 75 % of high-pollution events are primarily caused by local sources. But there is also a considerable influence from non-local (30 %) emissions from the transport sector during the post-monsoon season. Overall, we find that in the post-monsoon season, there is substantial accumulation of high local NOx emissions from the transport sector (70 % of total emissions, 70 % local), alongside the import of NOx pollution into Delhi (30 % non-local). This work indicates that both high local NOx emissions from the transport sector and the advection of highly polluted air originating from outside Delhi are of concern for the population. As a result, air quality mitigation strategies need to be adopted not only in Delhi but in the surrounding regions to successfully control this issue. In addition, our analysis suggests that the largest benefits to Delhi NOx air quality would be seen with targeted reductions in emissions from the transport and agricultural waste burning sectors, particularly during the post-monsoon season. [ABSTRACT FROM AUTHOR]

The current study aimed to measure real-world emissions of three-wheeled autorickshaws powered by CNG and parameters (such as speed, acceleration, air-fuel (A/F) ratio, and rpm) influencing 3-wheeler emission rates. Test vehicles manufactured under Bharat Standards BS-III and BS-IV were monitored for exhaust emissions in Delhi city using a portable exhaust emission measurement system (AVL Ditest Gas 1000). The average emission rates of CO, HC, and NO gases for on-road autorickshaws were found to be 0.015 ± 0.017, 0.003 ± 0.0017, and 0.007 ± 0.005 g/s, respectively. Further, the highest emission factor values of 3.98 g/km and 3.93 g/km were estimated for CO and HC+NO gases, respectively. These values were found to be 1.4–3.2 times higher than the respective BS emission norms (BS III-CO =1.25 g/km, HC+NO = 1.25 g/km; BS-IV-CO = 0.94 g/km and HC+NO = 0.94 g/km). In this study, it was observed that the driving pattern and emissions were affected by traffic characteristics, driver behavior (constant acceleration and deceleration), and vehicle characteristics. The air-fuel ratio (A/F) was found to correlate highly with emission rates, followed by acceleration/deceleration and speed. Further analysis found that more than 70% of the aggregated emissions were due to acceleration and deceleration, which contributed to nearly 70% of the travel time. This was followed by the breakdown of speed and emissions into different bins, which found that 20–30 kmph has a higher emission rate and 40–50 kmph bin has a lower emission rate. [ABSTRACT FROM AUTHOR]

Atmospheric ozone (O3) concentration is impacted by a number of factors, such as the amount of solar radiation, the composition of nitrogen oxides (NOx) and hydrocarbons, the transport of pollutants and the amount of particulate matter in the atmosphere. The oxidative potential of the atmosphere and the formation of secondary organic aerosols (SOAs) as a result of atmospheric oxidation are influenced by the prevalent O3 concentration. The formation of secondary aerosols from O3 depends on several meteorological, environmental and chemical factors. The relationship between PM2.5 and O3 in different urban environmental regimes of India is investigated in this study during the summer and winter seasons. A relationship between PM2.5 and O3 has been established for many meteorological and chemical variables, such as RH, WS, T and NOx, for the selected study locations. During the winter season, the correlation between PM2.5 and O3 was found to be negative for Delhi and Bengaluru, whereas it was positive in Ahmedabad. The city of Bengaluru was seen to have a positive correlation between PM2.5 and O3 during summer, coinciding with the transport of marine air masses with high RH and low wind speed (as evident from FLEXPART simulations), leading to the formation of SOAs. Further, O3 concentrations are predicted using a Recurrent Neural Network (RNN) model based on the relation obtained between PM2.5 and O3 for the summer season using NOx, T, RH, WS and PM2.5 as inputs. [ABSTRACT FROM AUTHOR]

Air quality information is not captured adequately due to limited numbers of air quality monitoring stations across many cities worldwide. Limited studies apply advanced spatial mapping techniques to predict pollutant concentrations in highly polluted regions with high spatial variability. This paper demonstrates an advanced detrending spatial mapping technique to assess the variations of particulate matter concentrations across different land use categories in a highly polluted city—Delhi—and estimate population-weighted average concentrations in the city. The "Detrended Kriging" method uses the city's monitored datasets and land use information to predict pollutant concentrations. Concentrations are detrended based on high-resolution local land use characteristics and then interpolated using ordinary kriging before retrending again. The model estimates population-weighted concentrations (more important for health exposures) of PM2.5 (113 µg/m3) and PM10 (248 µg/m3) for Delhi and finds them to be 21–36% higher than the monitored values in the crucial winter season of 2018. The model demonstrates satisfactory performance on both spatial and temporal scales in Delhi and shows high index of agreement (d = 0.86 for PM10 and 0.81 for PM2.5), low RMSE (27.3 µg/m3 for PM10 and 11.8 µg/m3 for PM2.5), and low bias (− 1.6 µg/m3 for PM10 and − 0.5 µg/m3 for PM2.5) for the detrended kriging model, in comparison to ordinary kriging (PM2.5 (d = 0.54, RMSE = 13.81, bias = − 0.86) and PM10 (d = 0.33, RMSE = 41.73, bias = − 4.7)) and inverse distance weighting method (PM2.5 (d = 0.65, RMSE = 16.08, bias = 2.93) and PM10 (d = 0.55, RMSE = 46.10, bias = 7.8)). Statistical measure "d" varies between 0 (no agreement) and 1 (perfect match). [ABSTRACT FROM AUTHOR]

The onset of the second wave of COVID-19 devastated many countries worldwide. Compared with the first wave, the second wave was more aggressive regarding infections and deaths. Numerous studies were conducted on the association of air pollutants and meteorological parameters during the first wave of COVID-19. However, little is known about their associations during the severe second wave of COVID-19. The present study is based on the air quality in Delhi during the second wave. Pollutant concentrations decreased during the lockdown period compared to pre-lockdown period (PM2.5: 67 µg m−3 (lockdown) versus 81 µg m−3 (pre-lockdown); PM10: 171 µg m−3 versus 235 µg m−3; CO: 0.9 mg m−3 versus 1.1 mg m−3) except ozone which increased during the lockdown period (57 µg m−3 versus 39 µg m−3). The variation in pollutant concentrations revealed that PM2.5, PM10 and CO were higher during the pre-COVID-19 period, followed by the second wave lockdown and the lowest in the first wave lockdown. These variations are corroborated by the spatiotemporal variability of the pollutants mapped using ArcGIS. During the lockdown period, the pollutants and meteorological variables explained 85% and 52% variability in COVID-19 confirmed cases and deaths (determined by General Linear Model). The results suggests that air pollution combined with meteorology acted as a driving force for the phenomenal growth of COVID-19 during the second wave. In addition to developing new drugs and vaccines, governments should focus on prediction models to better understand the effect of air pollution levels on COVID-19 cases. Policy and decision-makers can use the results from this study to implement the necessary guidelines for reducing air pollution. Also, the information presented here can help the public make informed decisions to improve the environment and human health significantly. [ABSTRACT FROM AUTHOR]

Isoprene and monoterpene emissions to the atmosphere are generally dominated by biogenic sources. The oxidation of these compounds can lead to the production of secondary organic aerosol; however the impact of this chemistry in polluted urban settings has been poorly studied. Isoprene and monoterpenes can form secondary organic aerosol (SOA) heterogeneously via anthropogenic–biogenic interactions, resulting in the formation of organosulfate (OS) and nitrooxy-organosulfate (NOS) species. Delhi, India, is one of the most polluted cities in the world, but little is known about the emissions of biogenic volatile organic compounds (VOCs) or the sources of SOA. As part of the DELHI-FLUX project, gas-phase mixing ratios of isoprene and speciated monoterpenes were measured during pre- and post-monsoon measurement campaigns in central Delhi. Nocturnal mixing ratios of the VOCs were substantially higher during the post-monsoon (isoprene: (0.65±0.43) ppbv; limonene: (0.59±0.11) ppbv; α -pinene: (0.13±0.12) ppbv) than the pre-monsoon (isoprene: (0.13±0.18) ppbv; limonene: 0.011±0.025 (ppbv); α -pinene: 0.033±0.009) period. At night, isoprene and monoterpene concentrations correlated strongly with CO during the post-monsoon period. Filter samples of particulate matter less than 2.5 µm in diameter (PM 2.5) were collected and the OS and NOS content analysed using ultra-high-performance liquid chromatography tandem mass spectrometry (UHPLC-MS 2). Inorganic sulfate was shown to facilitate the formation of isoprene OS species across both campaigns. Sulfate contained within OS and NOS species was shown to contribute significantly to the sulfate signal measured via AMS. Strong nocturnal enhancements of NOS species were observed across both campaigns. The total concentration of OS and NOS species contributed an average of (2.0±0.9) % and (1.8±1.4) % to the total oxidized organic aerosol and up to a maximum of 4.2 % and 6.6 % across the pre- and post-monsoon periods, respectively. Overall, this study provides the first molecular-level measurements of SOA derived from isoprene and monoterpene in Delhi and demonstrates that both biogenic and anthropogenic sources of these compounds can be important in urban areas. [ABSTRACT FROM AUTHOR]

The accent of the present study is determination of Urban Aerosol Pollution Island (UAPI) intensity and spatial variability in particulate matter concentration (PM10 and PM2.5) over Delhi. For analysis, the hourly concentration dataset of PM2.5 and PM10 from January 2019 to December 2020 was obtained from ten air quality monitoring stations of Delhi. Additionally, UAPI Index has been calculated to assess the intensity of particulate pollution. The daily, monthly, and annual variations in the trends of PM10, PM2.5, and UAPI index along with related meteorological parameters have been analyzed. Particulate pollution peaked majorly during two seasons, i.e., summer and winter. The highest concentration of PM10 was observed to be 426.77 µg/m3 while that of PM2.5 was observed to be 301.91 µg/m3 in January 2019 for traffic-affected regions. During winters, higher PM2.5 concentration was observed which can be ascribed to increased local emissions and enhanced secondary particle formations. While the increase in PM10 concentrations led to an increment in pollution episodes during summers over most of the sites in Delhi. The UAPI index was found to be declining in 2020 over traffic affected regions (77.92 and 27.22 for 2019 and 2020, respectively) as well as in the background regions (64.91 and 19.80 for 2019 and 2020, respectively) of Delhi. Low traffic intensity and reduced pollutant emission could have been responsible for the reduction of UAPI intensity in the year 2020. The result indicates that lockdown implemented to control the COVID-19 outbreak led to an unexpected decrease in the PM10 pollution over Delhi. [ABSTRACT FROM AUTHOR]

The main objective of this study is to recognize the limitations of available data, its usefulness to regulate pollution sources across National Capital Region (NCR) and create winter profiles of every region. Within a network of each air quality monitoring station in Delhi-NCR winters [2019 December–2020 January (Pre-Covid), and 2020 December–2021 January (Post-Covid)], daily-averaged PM10 and PM2.5, NOx, O3, SO2, CO and NH3 levels were found to be very consistent across Delhi and the NCR. The bivariate polar plots revealed that local sources dominate PM10 and PM2.5 concentrations across all monitoring locations in Delhi-NCR. The importance of the local source's superiority aids in the measurement of concentrations. We examined the various pollutants found during the winter season across Delhi-NCR for 2019–2020–2021. Plotting bivariate polar plots can reveal the relationship between air contaminants and meteorological conditions. This technique identified the concentration level and also helps in prediction. The preliminary results also suggested that the air pollutants considerably reduced in post-covid period as compared to the pre-covid period due to the restriction of traffic and commercial activities. [ABSTRACT FROM AUTHOR]

Delhi is one of the world's most polluted cities, with very high concentrations of airborne particulate matter. However, little is known about the factors controlling the characteristics of wide-range particle number size distributions. Here, new measurements are reported from three field campaigns conducted in winter and pre-monsoon and post-monsoon seasons at the Indian Institute of Technology campus in the south of the city. Particle number size distributions were measured simultaneously, using a scanning mobility particle sizer and a GRIMM optical particle monitor, covering 15 nm to >10 µm diameter. The merged, wide-range size distributions were categorized into the following five size ranges: nucleation (15–20 nm), Aitken (20–100 nm), accumulation (100 nm–1 µm), large fine (1–2.5 µm), and coarse (2.5–10 µm) particles. The ultrafine fraction (15–100 nm) accounts for about 52 % of all particles by number (PN 10 is the total particle number from 15 nm to 10 µm) but just 1 % by PM 10 volume (PV 10 is the total particle volume from 15 nm to 10 µm). The measured size distributions are markedly coarser than most from other parts of the world but are consistent with earlier cascade impactor data from Delhi. Our results suggest substantial aerosol processing by coagulation, condensation, and water uptake in the heavily polluted atmosphere, which takes place mostly at nighttime and in the morning hours. Total number concentrations are highest in winter, but the mode of the distribution is largest in the post-monsoon (autumn) season. The accumulation mode particles dominate the particle volume in autumn and winter, while the coarse mode dominates in summer. Polar plots show a huge variation between both size fractions in the same season and between seasons for the same size fraction. The diurnal pattern of particle numbers is strongly reflective of a road traffic influence upon concentrations, especially in autumn and winter, although other sources, such as cooking and domestic heating, may influence the evening peak. There is a clear influence of diesel traffic at nighttime, when it is permitted to enter the city, and also indications in the size distribution data of a mode < 15 nm, which is probably attributable to CNG/LPG vehicles. New particle formation appears to be infrequent and is, in this dataset, limited to 1 d in the summer campaign. Our results reveal that the very high emissions of airborne particles in Delhi, particularly from traffic, determine the variation in particle number size distributions. [ABSTRACT FROM AUTHOR]

Lockdown was imposed by the Indian government in the month of March 2020 as an early precaution to the COVID-19 pandemic which obstructed the socio-economic growth globally. The main aim of this study was to analyse the impact of lockdown (imposed in March and continued in April 2020) on the existing air quality in three megacities of India (Delhi, Mumbai and Kolkata) by assessing the trends of PM10 and NO2 concentrations. A comparison of the percentage reduction in concentrations of lockdown period with respect to same period in year 2019 and pre-lockdown period (February 14–March 24) was made. It was observed from the study that an overall decrease of pollutant concentrations was in the ranges of 30–60% and 52–80% of PM10 and NO2, respectively, in the three cities during lockdown in comparison with previous year and pre-lockdown period. The overall decrease in concentrations of pollutants at urban sites was greater than the background sites. Highest decline in concentrations of PM10 were observed in Kolkata city, followed by Mumbai and Delhi, while decline in NO2 was highest in Mumbai. Results also highlighted that capital city Delhi had the worst air quality amongst three cities, with particulate matter (PM10) being the dominant pollutant. Although COVID-19 has significantly affected the human life considering the mortality and morbidity, lockdowns imposed to control the pandemic had significantly improved the air quality in the selected study locations, although for the short amount of period. [ABSTRACT FROM AUTHOR]

Discusses the effacement of lesbian agentic positionalities in Delhi, India in the 1980s. Ways to reimagine local to transnational queer apparitions; Problems that representations or effacements pose for the sites being represented and not represented and for possible transversal queer alliances; Segregated queer identity production sites within the United States.

• CO, HC, and NO Emission factors for both diesel and petrol passenger cars are reported. • Passenger cars, tested on five study routes in Delhi have different emission factors. • Average emission factors of CO, HC, and NO were 3.8, 0.3, and 0.54 g/km, respectively. • Emission rates vary as changes in speed and acceleration rates. • Emission factors were lowest at 40–60 kmph speed range. In this study, researchers have explored real-world driving conditions and developed emission factors for 58 passenger cars using on-board emission measurement technique while driving on five different routes in Delhi. The measured average emission factors of CO, HC, and NO were 3.99, 0.34, and 0.54 g/km for diesel vehicles, 7.26, 0.17, and 0.62 for petrol vehicles respectively. Road, traffic, vehicle type, and driving characteristics affect the quantity of emissions released. However, speed and acceleration significantly impact emission rates increasing with the increase in speed and acceleration. Also, emissions were minimal at 40–60 kmph and −0.5–0.5 m/s2. The estimated city-wide CO, HC, and NO emissions were 60.8, 4.8, and 9.72tonnes/day. These results demonstrate the importance of monitoring the real-world exhaust emissions given the substantial difference between test cycle measurements used for compliance testing of new vehicles. [ABSTRACT FROM AUTHOR]