Year-long simulation of gaseous and particulate air pollutants in India

Kota, Sri Harsha; Guo, Hao; Myllyvirta, Lauri; Hu, Jianlin; Sahu, Shovan Kumar; Garaga, Rajyalakshmi; Ying, Qi; Gao, Aifang; Dahiya, Sunil; Wang, Yuan; Zhang, Hongliang

doi:10.1016/j.atmosenv.2018.03.003

Cited by 102 publications

(67 citation statements)

References 57 publications

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“…The performance of the model was especially good on days with high O 3 and PM 2.5 levels. De-tails of the model application and the performance in 2015 can be found in Kota et al (2018). Also, the source apportionment results are comparable with Sharma et al (2016) using positive matrix factorization (PMF) as Guo et al (2017) introduced.…”

Section: Methodssupporting

confidence: 63%

“…Comparison of methods and excess mortality by diseases and sources from this study with other studies in India. Lelieveld (2017) Table 2 shows the comparison of the results with other studies. This study predicted higher total premature mortality (1.04 million) compared to Lelieveld et al (2015) (0.65 million), Ghude et al (2016) (0.57 million), and Giannadaki et al (2016) (0.58 million), and comparable results compared to GBD MAPS Working Group (2018) (1.09 million) and Conibear et al (2018) (0.99 million).…”

Section: Predicted Premature Mortality and Yllmentioning

confidence: 97%

“…Lelieveld et al (2015) estimated the global premature mortality of chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), ischaemic heart disease (IHD) and lung cancer (LC) using predicted PM 2.5 concentrations from a global atmospheric model and exposure-response equations from Burnett et al (2014). The impacts of different sources on ambient PM 2.5 concentrations and the associated disease burden at global scale were also studied in Silva et al (2016) and Lelieveld (2017). Giannadaki et al (2016) and Conibear et al (2018) studied the health impacts from applying different air quality standards and explored the non-linear response of health impacts to PM 2.5 in India.…”

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confidence: 99%

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Source contributions and potential reductions to health effects of particulate matter in India

et al. 2018

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Abstract. Health effects of exposure to fine particulate matter (PM2.5) in India were estimated in this study based on a source-oriented version of the Community Multi-scale Air Quality (CMAQ) model. Contributions of different sources to premature mortality and years of life lost (YLL) were quantified in 2015. Premature mortality due to cerebrovascular disease (CEVD) was the highest in India (0.44 million), followed by ischaemic heart disease (IHD, 0.40 million), chronic obstructive pulmonary disease (COPD, 0.18 million), and lung cancer (LC, 0.01 million), with a total of 1.04 million deaths. The states with highest premature mortality were Uttar Pradesh (0.23 million), Bihar (0.12 million), and West Bengal (0.10 million). The highest total YLL was 2 years in Delhi, and the Indo-Gangetic plains and eastern India had higher YLL (∼1 years) than other regions. The residential sector was the largest contributor to PM2.5 concentrations (∼40 µg m−3), total premature mortality (0.58 million), and YLL (∼0.2 years). Other important sources included industry (∼20 µg m−3), agriculture (∼10 µg m−3), and energy (∼5 µg m−3) with their national averaged contributions of 0.21, 0.12, and 0.07 million to premature mortality, and 0.12, 0.1, and 0.05 years to YLL. Reducing PM2.5 concentrations would lead to a significant reduction of premature mortality and YLL. For example, premature mortality in Uttar Pradesh (including Delhi) due to PM2.5 exposures would be reduced by 79 % and YLL would be reduced by 83 % when reducing PM2.5 concentrations to 10 µg m−3.

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Section: Methodssupporting

confidence: 63%

Section: Predicted Premature Mortality and Yllmentioning

confidence: 97%

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confidence: 99%

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Source contributions and potential reductions to health effects of particulate matter in India

et al. 2018

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“…Approximately one million people died in 2015 due to ambient particulate matter (PM) pollution alone in India (Guo et al, 2017). Indian cities have been always making into the top 20 most polluted cities of the world for the past few years and exceeding the ambient air quality standards recommended by the World Health Organization and Central Pollution Control Board (CPCB) Kota et al, 2018;Mukherjee and Agrawal, 2018).…”

Section: Introductionmentioning

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

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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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“…To effectively design pollution control strategies, the contributions of different emission sources to PM 2.5 concentrations are crucial. Source-oriented chemical transport models (CTMs) based on a tagged tracer technique have been developed and used for source apportionment of gases (Kota et al, 2014) and PM (Zhang and Ying, 2010;Kota et al, 2015;Ying et al, 2015) in the past. Guo et al (2017), which was the first study to use the source-oriented Community Multiscale Air Quality (CMAQ) model in India, showed that the residential sector contributed the most (∼ 80 µg m −3 ) to total PM 2.5 , followed by the industrial sector (∼ 70 µg m −3 ) in 2015.…”

Section: Introductionmentioning

confidence: 99%

Response to reviewer 1

Zhang¹

2018

Preprint

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Health effects of exposure to fine particulate matter (PM 2.5) in India were estimated in this study based on a source-oriented version of the Community Multiscale Air Quality (CMAQ) model. Contributions of different sources to premature mortality and years of life lost (YLL) were quantified in 2015. Premature mortality due to cerebrovascular disease (CEVD) was the highest in India (0.44 million), followed by ischaemic heart disease (IHD, 0.40 million), chronic obstructive pulmonary disease (COPD, 0.18 million), and lung cancer (LC, 0.01 million), with a total of 1.04 million deaths. The states with highest premature mortality were Uttar Pradesh (0.23 million), Bihar (0.12 million), and West Bengal (0.10 million). The highest total YLL was 2 years in Delhi, and the Indo-Gangetic plains and eastern India had higher YLL (∼ 1 years) than other regions. The residential sector was the largest contributor to PM 2.5 concentrations (∼ 40 µg m −3), total premature mortality (0.58 million), and YLL (∼ 0.2 years). Other important sources included industry (∼ 20 µg m −3), agriculture (∼ 10 µg m −3), and energy (∼ 5 µg m −3) with their national averaged contributions of 0.21, 0.12, and 0.07 million to premature mortality, and 0.12, 0.1, and 0.05 years to YLL. Reducing PM 2.5 concentrations would lead to a significant reduction of premature mortality and YLL. For example, premature mortality in Uttar Pradesh (including Delhi) due to PM 2.5 exposures would be reduced by 79 % and YLL would be reduced by 83 % when reducing PM 2.5 concentrations to 10 µg m −3 .

show abstract

Year-long simulation of gaseous and particulate air pollutants in India

Cited by 102 publications

References 57 publications

Source contributions and potential reductions to health effects of particulate matter in India

Source contributions and potential reductions to health effects of particulate matter in India

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

Response to reviewer 1

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