Space-time variability of ambient PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; diurnal pattern over India from 18-years (2000–2017) of MERRA-2 reanalysis data

Bali, Kunal; Dey, Sagnik; Ganguly, Dilip; Smith, Kirk R.

doi:10.5194/acp-2019-731

Cited by 4 publications

(5 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The temporal, vertical, and horizontal (latitude × longitude) resolutions of the MERRA‐2 data were 1 hr, 2–3 km, and 0.5 × 0.625°, respectively (Global Modeling and Assimilation Office, 2015). The MERRA‐2 data have many applications in the energy budget of the polar atmosphere, planetary waves, aerosols, thermal tides, and climate variability (Bali et al., 2019; Tang et al., 2021; Ukhov et al., 2020). These studies showed that MERRA‐2 data had a high authenticity.…”

Section: Data Sets and Methodsmentioning

confidence: 99%

Interannual and Interhemispheric Comparisons of Q2DW Bimodal and Unimodal Structures During the 2003–2020 Summer Period

Tang,

2023

JGR Space Physics

View full text Add to dashboard Cite

By analyzing Sounding of the Atmosphere using Broadband Emission Radiometry and the Modern‐Era Retrospective analysis for Research Applications Version 2 reanalysis observations from 2003 to 2020, we found that the structural variation of the westward quasi‐2‐day wave (Q2DW) is related to the mean zonal wind in the background atmosphere, and the zonal wavenumber 3 (W3) is more affected by the background atmosphere than the zonal wavenumber 4 (W4). Our study focuses on the spatial structure of ∼50–95 km and ∼50°S–50°N. The spatial structure of W3 and W4 in the Northern and Southern Hemispheres is unimodal and bimodal, while the bimodal structure of W3 in the Southern Hemisphere is more obvious in some years. In the unimodal structure, W3 has a higher (altitude) peak in the Southern Hemisphere (∼82 km) than in the Northern Hemisphere (∼70 km), while W4 peaks at ∼70 km in both hemispheres. In the bimodal structure, W3 in the Southern Hemisphere fluctuates mostly at ∼82 km, followed by ∼68 km, while W3 in the Northern Hemisphere fluctuates mostly at ∼70 km, followed by ∼82 km. The spatial structure of W4 in both hemispheres fluctuates mainly at ∼70 km, followed by ∼82 km. In addition, W3 is stronger in the Southern Hemisphere than the Northern Hemisphere, W4 is stronger in the Northern Hemisphere than the Southern Hemisphere, and peak amplitudes of both W3 and W4 are larger in a bimodal structure than unimodal structure. The diagnostic analysis shows that the interannual and interhemispheric structure changes of Q2DWs may be due to the larger mean flow instabilities, background winds, and refractive index in the mesosphere, which make the bimodal structure of W3 and W4 obtain larger energy for propagation and amplification, resulting in higher (altitude) and larger amplitudes. This indicates that the background atmosphere of the mesosphere can affect the spatial structure of Q2DWs.

show abstract

Section: Data Sets and Methodsmentioning

confidence: 99%

Interannual and Interhemispheric Comparisons of Q2DW Bimodal and Unimodal Structures During the 2003–2020 Summer Period

Tang,

2023

JGR Space Physics

View full text Add to dashboard Cite

show abstract

“…Air pollution caused between 10000 and 30000 premature deaths in Delhi in 2015 (Bithal, 2018). While air pollution is particularly bad in winter over Delhi, the PM 2.5 levels are often higher than the WHO limit of 25 µg m −3 and the Indian Air Quality Standard of 60 µg m −3 for most part of year except during the rainy months of July and August (Bali et al, 2019;Hama et al, 2020). Main sources of PM 2.5 particles are anthropogenic emissions except when there is a dust storm like event.…”

Section: Aerosolsmentioning

confidence: 99%

“…Gadhavi et al (2015) who analyzed black carbon concentration over a rural location in South India, also found high concentrations of black carbon particles for the days when the source region extended to north-west of Delhi compared to other days. While looking at the PM 2.5 concentration maps available in the literature (Bali et al, 2019;Reddington et al, 2019), one can see whole of IGP region filled with high amount of particles. The temporal variation of PM concentration when looked in connotation with FLEXPART trajectories, east of Delhi region seems to contribute much smaller amount of air pollution compared to when wind is blowing from west.…”

Section: Aerosolsmentioning

confidence: 99%

Effect of Lockdown on Pollutant Levels in the Delhi Megacity: Role of Local Emission Sources and Chemical Lifetimes

Mallik

Gadhavi

Lal

et al. 2021

Front. Environ. Sci.

View full text Add to dashboard Cite

The COVID-19 pandemic resulted in changed emission regimes all over the world. India also imposed complete lockdown on all modes of travel and industrial activities for about 2 months from 25-March-2020 and later unlocked these activities in a phased manner. Here, we study signatures of emissions changes on levels of atmospheric trace gases and aerosols contributing to air pollution over multiple sites in India’s capital Delhi covering various lockdown and unlock phases using satellite data and in-situ observations. The resulting changes in the levels of these species were compared with respect to their average of 2015–2019 to attribute for year to year and seasonal changes. A clear impact of lockdown was observed for AOD, PM, NO2, CO, and SO2 as a result of emission changes, while changed precursor levels led to a change in O3 chemical regimes impacting its concentrations. A detailed analysis of FLEXPART trajectories revealed increased PM levels over Delhi in north-westerly air masses sourced to Punjab region all the way up to Pakistan. Changes in aerosols and NO2 were not only restricted to the surface but transcended the total tropospheric column. The maximum decrease in PM, NO2, CO, and SO2 was observed during the month of total lockdown in April. The lockdown impact varied with species e.g., PM10 and PM2.5 as well as locations even within the periphery of Delhi. While surface level aerosols and NO2 showed significant and almost similar changes, AOD showed much lower decrease than tropospheric column NO2.

show abstract

“…Sayeed et al (Sayeed et al, 2022) improved the PM2.5 concentration in the continental United States using Random Forest (RF) approach coped with meteorology and aerosol species of MERRA-2. Some studies have demonstrated the feasibility of tree-based model to estimate PM2.5 concentrations in India (Kumar et al, 2023;Dhandapani et al, 2023;Bali et al, 2019). However, it is challenging to establish long-term, full-coverage, high accuracy, open-source PM data products in India due to insufficient model robustness and implementation capacity (Dey et al, 2020;Kumar et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Reconstructing long-term (1980–2022) daily ground particulate matter datasets in India (LongPMInd)

Wang,

Zhang,

Zhao

et al. 2024

Preprint

View full text Add to dashboard Cite

Abstract. Severe airborne particulate matter (PM, including PM2.5 and PM10) pollution in India has caused widespread concern. Accurate PM datasets are fundamental for scientific policymaking and health impact assessment, while surface observations in India are limited due to scarce sites and uneven distribution. In this work, a simple structured, efficient, and robust model based on the Light Gradient Boosting Machine (LightGBM) was developed to fuse multi-source data and estimate long-term (1980–2022) historical daily ground PM datasets in India (LongPMInd). The LightGBM model shows good accuracy with out-of-sample, out-of-site, and out-of-year cross-validation CV test R2 of 0.77, 0.70, and 0.66, respectively. Small performance gaps between PM2.5 training and testing (delta RMSE of 1.06, 3.83, and 7.74 μg m-3) indicate low overfitting risks. With great generalization ability, the open-accessible, long-term, and high-quality daily PM2.5 and PM10 products were then reconstructed (10 km, 1980–2022). It shows that India has experienced severe PM pollution in the Indo-Gangetic Plain (IGP), especially in winter. PM concentrations significantly increased (p<0.05) in most regions since 2000 (0.34 μg m-3 year-1). The turning point occurred in 2018 when the Indian government launched the National Clean Air Program, PM2.5 concentrations declined in most regions (- 0.78 μg m-3 year-1) during 2018–2022. Severe PM2.5 pollution caused continuous increased attributable premature mortalities, from 0.73 (95 % CI: 0.65–0.80) million in 2000 to 1.22 (95 % CI: 1.03–1.41) million in 2019, particularly in the IGP, where attributable mortality increased from 0.36 to 0.60 million. The LongPMInd datasets have the potential to support multi-applications of air quality management, public health, and climate change. The daily and monthly PM2.5 and PM10 datasets are publicly accessible at https://doi.org/10.5281/zenodo.10073944 (Wang et al., 2023a).

show abstract

Space-time variability of ambient PM<sub>2.5</sub> diurnal pattern over India from 18-years (2000–2017) of MERRA-2 reanalysis data

Cited by 4 publications

References 23 publications

Interannual and Interhemispheric Comparisons of Q2DW Bimodal and Unimodal Structures During the 2003–2020 Summer Period

Interannual and Interhemispheric Comparisons of Q2DW Bimodal and Unimodal Structures During the 2003–2020 Summer Period

Effect of Lockdown on Pollutant Levels in the Delhi Megacity: Role of Local Emission Sources and Chemical Lifetimes

Reconstructing long-term (1980–2022) daily ground particulate matter datasets in India (LongPMInd)

Contact Info

Product

Resources

About