Sensitivity analysis of ground level ozone in India using WRF-CMAQ models

Sharma, Sumit; Chatani, Satoru; Mahtta, Richa; Goel, Anuj Kumar; Kumar, Atul

doi:10.1016/j.atmosenv.2016.01.036

Cited by 83 publications

(74 citation statements)

References 41 publications

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“…It could become even higher because meteorological fields simulated with the ECMWF data set would give lower NO 2 concentrations than the NCEP data set used by Jena et al (). The annual total NO x emission used in this study is 1.6 TgN in 2010 (Sharma et al, ). It may be underestimated if growth of economic activities during 2005–2010 is taken into account (Mahajan et al, ).…”

Section: Resultsmentioning

confidence: 99%

“…They also optimized NO x emissions by minimizing the deviations between simulated concentrations and satellite observations. Sharma et al () investigated the sensitivities of NO x and volatile organic compound emissions to surface ozone concentrations over India using WRF and the Community Multiscale Air Quality (CMAQ) modeling system (Byun & Schere, ). Sarkar et al () developed the technology‐linked nonmethane volatile organic compound emission inventory with chemical speciation for India.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainties Caused by Major Meteorological Analysis Data Sets in Simulating Air Quality Over India

Chatani

Sharma

2018

JGR Atmospheres

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Many places in India suffer from severe air pollution. Regional air quality simulations are essential to develop effective strategies for improving air quality, considering the nonlinear relationships between ambient pollutants and their precursor emissions. Meteorological fields used in simulations are derived from regional meteorological models with analysis data sets as inputs. This study reveals that two major analysis data sets provided by the National Centers for Environmental Prediction and the European Centre for Medium‐Range Weather Forecasts (ECMWF) cause significant differences in simulated meteorological fields over India. Especially, relative humidity values simulated using the ECMWF data set are much higher and closer to the observed values than those simulated using the National Centers for Environmental Prediction data set. Results simulated using the ECMWF data set show better model performance for most meteorological parameters over India. Differences in relative humidity originate in the data contained in the analysis data sets through grid nudging. It is not possible to avoid this underestimation by simply turning off grid nudging. The meteorological fields simulated with two major analysis data sets also lead to differences in pollutant concentrations simulated by regional chemical transport models through various physical and chemical processes. Differences originating in the two analysis data sets could be comparable with the uncertainties originating in various emission inventories in some regions and seasons in India. However, discrepancies between observed and simulated pollutant concentrations cannot be explained only by differences of the meteorological fields. Other aspects need to be explored for better performance required to develop effective strategies for India, based on accurate regional air quality simulations.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncertainties Caused by Major Meteorological Analysis Data Sets in Simulating Air Quality Over India

Chatani

Sharma

2018

JGR Atmospheres

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“…Previous studies have found Indian O 3 concentrations to be larger downwind of heavily populated regions relative to urban areas (Lal et al, ; Lawrence & Lelieveld, ). The combination of EDGAR‐HTAP with MOZART has also been found to enhance surface O 3 mixing ratios due to vertical mixing of enhanced O 3 that has been produced aloft (S. Sharma et al, ). The overestimation of O 3 could also be due to our underestimation of dust aerosols (Conibear et al, ).…”

Section: Methodsmentioning

confidence: 99%

Current and Future Disease Burden From Ambient Ozone Exposure in India

et al. 2018

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Long‐term ambient ozone (O3) exposure is a risk factor for human health. We estimate the source‐specific disease burden associated with long‐term O3 exposure in India at high spatial resolution using updated risk functions from the American Cancer Society Cancer Prevention Study II. We estimate 374,000 (95UI: 140,000–554,000) annual premature mortalities using the updated risk function in India in 2015, 200% larger than estimates using the earlier American Cancer Society Cancer Prevention Study II risk function. We find that land transport emissions dominate the source contribution to this disease burden (35%), followed by emissions from power generation (23%). With no change in emissions by 2050, we estimate 1,126,000 (95UI: 421,000–1,667,000) annual premature mortalities, an increase of 200% relative to 2015 due to population aging and growth increasing the number of people susceptible to air pollution. We find that the International Energy Agency New Policy Scenario provides small changes (+1%) to this increasing disease burden from the demographic transition. Under the International Energy Agency Clean Air Scenario we estimate 791,000 (95UI: 202,000–1,336,000) annual premature mortalities in 2050, avoiding 335,000 annual premature mortalities (45% of the increase) compared to the scenario of no emission change. Our study highlights that critical public health benefits are possible with stringent emission reductions, despite population growth and aging increasing the attributable disease burden from O3 exposure even under such strong emission reductions. The disease burden attributable to ambient fine particulate matter exposure dominates that from ambient O3 exposure in the present day, while in the future, they may be similar in magnitude.

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“…Increases in O 3 and CO and decreases in NO and NO 2 were observed in central India. Most cities in northern, western and southern regions are VOC limited (Sharma et al, 2016), thus this increase in O 3 could be due to more decrease in NOx compared to VOC. Furthermore, this could also be attributed to decrease in PM concentrations, which can result in more sunlight passing through atmosphere encouraging more photochemical activities and thus higher O 3 production (Dang and Liao, 2019;K.…”

Section: Change In Concentrations Of Pollutantsmentioning

confidence: 99%

Effect of restricted emissions during COVID-19 on air quality in India

Sharma

Zhang

Anshika

et al. 2020

Science of The Total Environment

991

119

997

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The effectiveness and cost are always top factors for policy-makers to decide control measures and most measures had no pre-test before implementation. Due to the COVID-19 pandemic, human activities are largely restricted in many regions in India since mid-March of 2020, and it is a progressing experiment to testify effectiveness of restricted emissions. In this study, concentrations of six criteria pollutants, PM 10 , PM 2.5 , CO, NO 2 , ozone and SO 2 during March 16th to April 14th from 2017 to 2020 in 22 cities covering different regions of India were analysed. Overall, around 43, 31, 10, and 18% decreases in PM 2.5 , PM 10 , CO, and NO 2 in India were observed during lockdown period compared to previous years. While, there were 17% increase in O 3 and negligible changes in SO 2 . The air quality index (AQI) reduced by 44, 33, 29, 15 and 32% in north, south, east, central and western India, respectively. Correlation between cities especially in northern and eastern regions improved in 2020 compared to previous years, indicating more significant regional transport than previous years. The mean excessive risks of PM reduced by~52% nationwide due to restricted activities in lockdown period. To eliminate the effects of possible favourable meteorology, the WRF-AERMOD model system was also applied in Delhi-NCR with actual meteorology during the lockdown period and an un-favourable event in early November of 2019 and results show that predicted PM 2.5 could increase by only 33% in unfavourable meteorology. This study gives confidence to the regulatory bodies that even during unfavourable meteorology, a significant improvement in air quality could be expected if strict execution of air quality control plans is implemented.

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Sensitivity analysis of ground level ozone in India using WRF-CMAQ models

Cited by 83 publications

References 41 publications

Uncertainties Caused by Major Meteorological Analysis Data Sets in Simulating Air Quality Over India

Uncertainties Caused by Major Meteorological Analysis Data Sets in Simulating Air Quality Over India

Current and Future Disease Burden From Ambient Ozone Exposure in India

Effect of restricted emissions during COVID-19 on air quality in India

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