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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]

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]

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]

This paper describes the development of artificial neural network (ANN) based carbon monoxide (CO) persistence (ANNCOP) models to forecast 8-h average CO concentration using 1-h maximum predicted CO data for the critical (winter) period (November–March). The models have been developed for three 8-h groupings of 10 p.m. to 6 a.m., 6 a.m. to 2 p.m. and 2–10 p.m., at two air quality control regions (AQCRs) in Delhi city, representing an urban intersection and an arterial road consisting heterogeneous traffic flows. The result indicates that time grouping of 2–10 pm is dominantly affected by inversion conditions and peak traffic flow. The ANNCOP model corresponding to this grouping predicts the 8-h average CO concentrations within the accuracy range of 68–71%. The CO persistence values derived from ANNCOP model are comparable with the persistence values as suggested by the Environmental Protection Agency (EPA), USA. This work demonstrates that ANN based model is capable of describing winter period CO persistence phenomena. [ABSTRACT FROM AUTHOR]