Dataset Information

PM2.5 diminution and haze events over Delhi during the COVID-19 lockdown period: an interplay between the baseline pollution and meteorology.

ABSTRACT: Delhi, a tropical Indian megacity, experiences one of the most severe air pollution in the world, linked with diverse anthropogenic and biomass burning emissions. First phase of COVID-19 lockdown in India, implemented during 25 March to 14 April 2020 resulted in a dramatic near-zeroing of various activities (e.g. traffic, industries, constructions), except the "essential services". Here, we analysed variations in the fine particulate matter (PM2.5) over the Delhi-National Capital Region. Measurements revealed large reductions (by 40-70%) in PM2.5 during the first week of lockdown (25-31 March 2020) as compared to the pre-lockdown conditions. However, O3 pollution remained high during the lockdown due to non-linear chemistry and dynamics under low aerosol loading. Notably, events of enhanced PM2.5 levels (300-400 µg m-3) were observed during night and early morning hours in the first week of April after air temperatures fell close to the dew-point (~?15-17 °C). A haze formation mechanism is suggested through uplifting of fine particles, which is reinforced by condensation of moisture following the sunrise. The study highlights a highly complex interplay between the baseline pollution and meteorology leading to counter intuitive enhancements in pollution, besides an overall improvement in air quality during the COVID-19 lockdown in this part of the world.

SUBMITTER: Dhaka SK

PROVIDER: S-EPMC7417527 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

PM<sub>2.5</sub> diminution and haze events over Delhi during the COVID-19 lockdown period: an interplay between the baseline pollution and meteorology.

Dhaka Surendra K SK Chetna Kumar Vinay V Panwar Vivek V Dimri A P AP Singh Narendra N Patra Prabir K PK Matsumi Yutaka Y Takigawa Masayuki M Nakayama Tomoki T Yamaji Kazuyo K Kajino Mizuo M Misra Prakhar P Hayashida Sachiko S

Scientific reports 20200810 1

Delhi, a tropical Indian megacity, experiences one of the most severe air pollution in the world, linked with diverse anthropogenic and biomass burning emissions. First phase of COVID-19 lockdown in India, implemented during 25 March to 14 April 2020 resulted in a dramatic near-zeroing of various activities (e.g. traffic, industries, constructions), except the "essential services". Here, we analysed variations in the fine particulate matter (PM<sub>2.5</sub>) over the Delhi-National Capital Regi ...[more]

PMID: 32778673

Similar Datasets

Project description:Ambient particulate matter of aerodynamic diameter less than 2.5 microns PM2.5) levels in Delhi routinely exceed World Health Organization (WHO) guidelines and Indian National Ambient Air Quality Standards (NAAQS) for acceptable levels of daily exposure. Only a handful of studies have examined the short-term mortality effects of PM in India, with none from Delhi examining the contribution of PM2.5.ObjectivesWe aimed to analyze the association between short-term PM2.5 exposures and daily nonaccidental mortality in Delhi, India.MethodsUsing generalized additive Poisson regression models, we examined the association between daily PM2.5 exposures and nonaccidental mortality between June 2010 and December 2016. Daily exposures to PM2.5 were estimated using an ensemble averaging technique developed by our research group, and mortality data were obtained from the Municipal Corporations of Delhi and the New Delhi Municipal Council.ResultsMedian exposures to PM2.5 were 91.1 µg/m3 (interquartile range = 68.9, 126.2), with minimum and maximum exposures of 21.4 µg/m3 and 276.7 µg/m3, respectively. Total nonaccidental deaths recorded in Delhi during the study period were 700,512. Each 25 µg/m3 increment in exposure was associated with a 0.8% (95% confidence intervals [CI] = 0.3, 1.3%) increase in daily nonaccidental mortality in the study population and a 1.5% (95% CI = 0.8, 2.2%) increase in mortality among those with 60 years of age or over. The exposure-response relationship was nonlinear in nature, with relative risk rising rapidly before tapering off above 125 µg/m3. Meeting WHO guidelines for acceptable levels of exposure over the study period would have likely averted 17,526 (95% CI = 6,837, 25,589) premature deaths, with older and male populations disproportionately affected.DiscussionThis study provides robust evidence of the impact of short-term exposure to PM2.5 on nonaccidental mortality with important considerations for various stakeholders including policymakers and physicians. Most importantly, we find that reducing exposures significantly below current levels would substantially decrease the mortality burden associated with PM2.5.

Project description:Elevated levels of ambient air pollution has been implicated as a major risk factor for morbidities and premature mortality in India, with particularly high concentrations of particulate matter in the Indo-Gangetic plain. High resolution spatiotemporal estimates of such exposures are critical to assess health effects at an individual level. This article retrospectively assesses daily average PM2.5 exposure at 1 km × 1 km grids in Delhi, India from 2010-2016, using multiple data sources and ensemble averaging approaches. We used a multi-stage modeling exercise involving satellite data, land use variables, reanalysis based meteorological variables and population density. A calibration regression was used to model PM2.5: PM10 to counter the sparsity of ground monitoring data. The relationship between PM2.5 and its spatiotemporal predictors was modeled using six learners; generalized additive models, elastic net, support vector regressions, random forests, neural networks and extreme gradient boosting. Subsequently, these predictions were combined under a generalized additive model framework using a tensor product based spatial smoothing. Overall cross-validated prediction accuracy of the model was 80% over the study period with high spatial model accuracy and predicted annual average concentrations ranging from 87 to 138 μg/m3. Annual average root mean squared errors for the ensemble averaged predictions were in the range 39.7-62.7 μg/m3 with prediction bias ranging between 4.6-11.2 μg/m3. In addition, tree based learners such as random forests and extreme gradient boosting outperformed other algorithms. Our findings indicate important seasonal and geographical differences in particulate matter concentrations within Delhi over a significant period of time, with meteorological and land use features that discriminate most and least polluted regions. This exposure assessment can be used to estimate dose response relationships more accurately over a wide range of particulate matter concentrations.

Dataset Information

PM2.5 diminution and haze events over Delhi during the COVID-19 lockdown period: an interplay between the baseline pollution and meteorology.

Publications

PM<sub>2.5</sub> diminution and haze events over Delhi during the COVID-19 lockdown period: an interplay between the baseline pollution and meteorology.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets