Effects of agriculture crop residue burning on aerosol properties and long-range transport over northern India: A study using satellite data and model simulations

Vijaykumar, K.; Safai, P.D.; Devara, P. C. S.; Rao, S. Vijaya Bhaskara; Jayasankar, C.K.

doi:10.1016/j.atmosres.2016.04.003

Cited by 46 publications

(16 citation statements)

References 49 publications

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“…The seasonal variability in the PBLH along with that in wind speed resulted in the ventilation coefficient (VC = PBLH × wind speed, sometimes referred to as normalized dilution rate) being 4-6 times slower for the wintertime compared to the summer. The VC is often used as a parameter to characterize the role of atmospheric dilution in pollutant concentrations, both in the Indian context (Vittal Murty et al, 1980) and globally (Marshall et al, 2005;Apte et al, 2012). Seasonal variability in the VC appears to reasonably agree with the higher NR-PM 1 concentrations in less ventilated cold months and lower concentrations in the warmer months when the VC was higher (Fig.…”

Section: Role Of Meteorologymentioning

confidence: 64%

Submicron aerosol composition in the world's most polluted megacity: the Delhi Aerosol Supersite study

et al. 2019

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Abstract. Delhi, India, routinely experiences some of the world's highest urban particulate matter concentrations. We established the Delhi Aerosol Supersite study to provide long-term characterization of the ambient submicron aerosol composition in Delhi. Here we report on 1.25 years of highly time-resolved speciated submicron particulate matter (PM1) data, including black carbon (BC) and nonrefractory PM1 (NR-PM1), which we combine to develop a composition-based estimate of PM1 (“C-PM1” = BC + NR-PM1) concentrations. We observed marked seasonal and diurnal variability in the concentration and composition of PM1 owing to the interactions of sources and atmospheric processes. Winter was the most polluted period of the year, with average C-PM1 mass concentrations of ∼210 µg m−3. The monsoon was hot and rainy, consequently making it the least polluted (C-PM1 ∼50 µg m−3) period. Organics constituted more than half of the C-PM1 for all seasons and times of day. While ammonium, chloride, and nitrate each were ∼10 % of the C-PM1 for the cooler months, BC and sulfate contributed ∼5 % each. For the warmer periods, the fractional contribution of BC and sulfate to C-PM1 increased, and the chloride contribution decreased to less than 2 %. The seasonal and diurnal variation in absolute mass loadings were generally consistent with changes in ventilation coefficients, with higher concentrations for periods with unfavorable meteorology – low planetary boundary layer height and low wind speeds. However, the variation in C-PM1 composition was influenced by temporally varying sources, photochemistry, and gas–particle partitioning. During cool periods when wind was from the northwest, episodic hourly averaged chloride concentrations reached 50–100 µg m−3, ranking among the highest chloride concentrations reported anywhere in the world. We estimated the contribution of primary emissions and secondary processes to Delhi's submicron aerosol. Secondary species contributed almost 50 %–70 % of Delhi's C-PM1 mass for the winter and spring months and up to 60 %–80 % for the warmer summer and monsoon months. For the cooler months that had the highest C-PM1 concentrations, the nighttime sources were skewed towards primary sources, while the daytime C-PM1 was dominated by secondary species. Overall, these findings point to the important effects of both primary emissions and more regional atmospheric chemistry on influencing the extreme particle concentrations that impact the Delhi megacity region. Future air quality strategies considering Delhi's situation in both a regional and local context will be more effective than policies targeting only local, primary air pollutants.

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Section: Role Of Meteorologymentioning

confidence: 64%

Submicron aerosol composition in the world's most polluted megacity: the Delhi Aerosol Supersite study

et al. 2019

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“…In the last decade, many countries of Asia experienced increasing air pollution from both industrial and motor vehicle emissions [ 65 ]. The pollution also comes from burning of crop residues and bush fires in various Asian countries [ 66 , 67 ]. Our findings suggest that there is a need for intervention to reduce air pollution in Asian countries and thereby limit premature mortality.…”

Section: Discussionmentioning

confidence: 99%

Chronic respiratory disease mortality and its associated factors in selected Asian countries: evidence from panel error correction model

2021

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Background Chronic Respiratory Diseases (CRDs) in Asian countries are a growing concern in terms of morbidity and mortality. However, a systematic understanding of the increasing age-adjusted mortality rate of chronic respiratory disease (CRD) and its associated factors is not readily available for many Asian countries. We aimed to determine country-level factors affecting CRD mortality using a panel error correction model. Methods Based on data from the Global Burden of Disease Study 2017, we estimated the trends and distribution of CRD mortality for selected Asian countries from 2010 to 2017. Furthermore, we evaluated the relationship between CRD mortality and Gross Domestic Product (GDP) per capita, average years of schooling, urbanization, and pollutant emission (PM2.5 concentration) using a fixed-effect model. We corrected the estimates for heteroscedasticity and autocorrelation through Prais-Winsten adjustment along with robust standard error. Results Between 2010 and 2017, approximately 21.4 million people died from chronic respiratory diseases in the countries studied. Age-standardized crude mortality rate from CRDs in the period had minimum and maximum values of 8.19 (Singapore in 2016) and 155.42 (North Korea in 2010) per 100,000 population, respectively. The coefficients corrected for autocorrelation and heteroskedasticity based on the final model of our study (Prais-Winsten), showed that all explanatory variables were statistically significant (p < 0.001). The model shows that the 1% increase in GDP per capita results in a 20% increase (0.203) in the CRD mortality rate and that a higher concentration of air pollution is also positively associated with the CRD deaths (0.00869). However, an extra year of schooling reduces the mortality rate by 4.79% (− 0.0479). Further, rate of urbanization is negatively associated with the CRD death rate (− 0.0252). Conclusions Our results indicate that both socioeconomic and environmental factors impact CRD mortality rates. Mortality due to CRD increases with rising GDP per capita and decreases with the percentage of the total population residing in urban areas. Further, mortality increases with greater exposure to PM2.5. Also, higher years of schooling mitigate rising CRD mortality rates, showing that education can act as a safety net against CRD mortality. These results are an outcome of sequential adjustments in the final model specification to correct for heteroscedasticity and autocorrelation.

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“…S4).These results suggest that plume rise in the model release the emissions too high or the model did not mix the smoke down fast enough. Vijayakumar et al (2016) showed agricultural fires can transport via upper troposphere and subside 385 over Delhi using ECMWF map. Social reasons can be also important as the first reaction of people during hazy days is to drive to work which directly (exhaust emission) and indirectly (road dusts) worsen air pollution.…”

Section: Extreme Pollution Episode Analysis 340mentioning

confidence: 99%

Improving regional air quality predictions in the Indo-Gangetic Plain-Case study of an intensive pollution episode in November 2017

Roozitalab¹,

Carmichael²,

Guttikunda³

2020

Preprint

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Abstract. Indo-Gangetic Plain (IGP) experienced an intensive air pollution episode during November 2017. Weather Research and Forecasting model with Chemistry (WRF-Chem), a coupled meteorology–chemistry model, was used to simulate this episode. In order to capture PM2.5 peaks, we modified input chemical boundary conditions and biomass burning emissions. CAM-Chem and MERRA-2 global models provided gaseous and aerosol chemical boundary conditions, respectively. We also incorporated VIIRS active fire points to fill missing fire emissions in FINN and scaled by a factor of seven for an 8-days period. Evaluations against various observations indicated the model captured the temporal trend very well although missed the peaks on Nov. 7th, 8th, and 10th. Modeled aerosol composition in Delhi showed Secondary Inorganic Aerosols (SIA) and Secondary Organic Aerosols (SOA) comprised 30 % and 27 % of total PM2.5 concentration, respectively, during November, with a modeled OC / BC ratio of 2.72. Back trajectories showed agricultural fires in Punjab were the major source for extremely polluted days in Delhi. Furthermore, high concentrations above the boundary layers in vertical profiles suggested either the plume rise in the model released the emissions too high, or the model did not mix the smoke down fast enough. Results also showed long-range transported dusts did not affect Delhi's air quality during the episode. Spatial plots showed averaged Aerosol Optical Depth (AOD) of 0.58 (± 0.4) over November. The model AODs were biased high over central India and low over eastern IGP, indicating improving emissions in eastern IGP can significantly improve the air quality predictions. We also found high ozone concentrations over the domain, which indicates ozone should be considered in future air quality management strategies alongside particulate matters.

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Effects of agriculture crop residue burning on aerosol properties and long-range transport over northern India: A study using satellite data and model simulations

Cited by 46 publications

References 49 publications

Submicron aerosol composition in the world's most polluted megacity: the Delhi Aerosol Supersite study

Submicron aerosol composition in the world's most polluted megacity: the Delhi Aerosol Supersite study

Chronic respiratory disease mortality and its associated factors in selected Asian countries: evidence from panel error correction model

Improving regional air quality predictions in the Indo-Gangetic Plain-Case study of an intensive pollution episode in November 2017

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