Articles | Volume 21, issue 5
https://doi.org/10.5194/acp-21-4025-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-4025-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Mar 2021
Research article |
| 17 Mar 2021
| 17 Mar 2021
Impact of reduced anthropogenic emissions during COVID-19 on air quality in India
Mengyuan Zhang
Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
Arpit Katiyar
Department of Civil Engineering, Indian Institute of Technology, Delhi, 110016, India
Shengqiang Zhu
Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
Juanyong Shen
School of Environmental Science and Engineering, Shanghai Jiao Tong
University, Shanghai 200240, China
Department of Civil and Environmental Engineering, The Hong Kong
Polytechnic University, Hong Kong SAR, 99907, China
Jinlong Ma
Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
Sri Harsha Kota
Department of Civil Engineering, Indian Institute of Technology, Delhi, 110016, India
Department of Civil and Environmental Engineering, The Hong Kong
Polytechnic University, Hong Kong SAR, 99907, China
Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
Institute of Eco-Chongming (IEC), Shanghai 200062, China
Related authors
No articles found.
Qianqian Gao, Guochao Chen, Xiaohui Lu, Jianmin Chen, Hongliang Zhang, and Xiaofei Wang
Atmos. Chem. Phys., 25, 12657–12673, https://doi.org/10.5194/acp-25-12657-2025, https://doi.org/10.5194/acp-25-12657-2025, 2025
Short summary
Short summary
Numerous lakes are shrinking, owing to climate change and human activities, releasing pollutants from dried lake beds as dust aerosols. The health risks remain unclear. Recently, Poyang and Dongting lakes faced record droughts, exposing 99 % and 88 % of their areas. We show that lake bed dust can raise PM10 to 637.5 μg m-³ and exceed non-carcinogenic (HQ = 4.13) and Cr carcinogenic (approx. 2.10 × 10−⁶) risk thresholds, posing growing health threats.
Xinlu Liu, Zhongyi Sun, Chong Shi, Peng Wang, Tangzhe Nie, Qingnan Chu, Huazhe Shang, Lu Sun, Dabin Ji, Meng Guo, Kunpeng Yi, Zhenghong Tan, Lan Wu, Xinchun Lu, and Shuai Yin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-588, https://doi.org/10.5194/essd-2025-588, 2025
Preprint under review for ESSD
Short summary
Short summary
Estimates of global biomass burning emissions differ, posing a challenge for environment and climate change research. In response to this challenge, our new 2003–2023 dataset integrates top-down and bottom-up methods with multi-source data. This provides a plausible emissions range to quantify uncertainty, revealing that the greatest uncertainty is not in traditional hotspots but in regions with infrequent, extreme fires. This work offers vital data for more robust climate models.
Mingjie Kang, Hongliang Zhang, and Qi Ying
Atmos. Chem. Phys., 25, 11453–11467, https://doi.org/10.5194/acp-25-11453-2025, https://doi.org/10.5194/acp-25-11453-2025, 2025
Short summary
Short summary
This study examines the impacts of reducing nitrogen oxides and volatile organic compounds on ozone (O3), secondary inorganic aerosols (SIAs), and OH and NO3 radicals. The results show similar predictions for 8 h O3 but significant variability for SIA and radicals, with differences up to 30 % for SIA and 200 % for radicals across chemical mechanisms and inventories. The findings highlight that evaluating control strategies for SIA and atmospheric oxidation capacity requires an ensemble approach.
Bin Luo, Yuqiang Zhang, Tao Tang, Hongliang Zhang, Jianlin Hu, Jiangshan Mu, Wenxing Wang, and Likun Xue
Atmos. Chem. Phys., 25, 4767–4783, https://doi.org/10.5194/acp-25-4767-2025, https://doi.org/10.5194/acp-25-4767-2025, 2025
Short summary
Short summary
India is facing a severe air pollution crisis that poses significant health risks, particularly from PM2.5 and O3. Our study reveals rising levels of both pollutants from 1995 to 2014, leading to increased premature mortality. While anthropogenic emissions play a significant role, biomass burning also impacts air quality, in particular seasons and regions in India. This study underscores the urgent need for localized policies to protect public health amid escalating environmental challenges.
Mingxue Li, Men Xia, Chunshui Lin, Yifan Jiang, Weihang Sun, Yurun Wang, Yingnan Zhang, Maoxia He, and Tao Wang
Atmos. Chem. Phys., 25, 3753–3764, https://doi.org/10.5194/acp-25-3753-2025, https://doi.org/10.5194/acp-25-3753-2025, 2025
Short summary
Short summary
Our field campaigns observed a strong diel pattern of chloroacetic acid as well as a strong correlation between its level and that of reactive chlorine species at a coastal site. Using quantum chemical calculations and box model simulation with an updated Master Chemical Mechanism, we found that the formation pathway of chloroacetic acid involved multiphase processes. Our study enhances understanding of atmospheric organic chlorine chemistry and emphasizes the importance of multiphase reactions.
Jianing Dai, Guy P. Brasseur, Mihalis Vrekoussis, Maria Kanakidou, Kun Qu, Yijuan Zhang, Hongliang Zhang, and Tao Wang
Atmos. Chem. Phys., 24, 12943–12962, https://doi.org/10.5194/acp-24-12943-2024, https://doi.org/10.5194/acp-24-12943-2024, 2024
Short summary
Short summary
This paper employs a regional chemical transport model to quantify the sensitivity of air pollutants and photochemical parameters to specified emission reductions in China for representative winter and summer conditions. The study provides insights into further air quality control in China with reduced primary emissions.
Shuai Wang, Mengyuan Zhang, Hui Zhao, Peng Wang, Sri Harsha Kota, Qingyan Fu, Cong Liu, and Hongliang Zhang
Earth Syst. Sci. Data, 16, 3565–3577, https://doi.org/10.5194/essd-16-3565-2024, https://doi.org/10.5194/essd-16-3565-2024, 2024
Short summary
Short summary
Long-term, open-source, gap-free daily ground-level PM2.5 and PM10 datasets for India (LongPMInd) were reconstructed using a robust machine learning model to support health assessment and air quality management.
Shuai Wang, Mengyuan Zhang, Yueqi Gao, Peng Wang, Qingyan Fu, and Hongliang Zhang
Geosci. Model Dev., 17, 3617–3629, https://doi.org/10.5194/gmd-17-3617-2024, https://doi.org/10.5194/gmd-17-3617-2024, 2024
Short summary
Short summary
Numerical models are widely used in air pollution modeling but suffer from significant biases. The machine learning model designed in this study shows high efficiency in identifying such biases. Meteorology (relative humidity and cloud cover), chemical composition (secondary organic components and dust aerosols), and emission sources (residential activities) are diagnosed as the main drivers of bias in modeling PM2.5, a typical air pollutant. The results will help to improve numerical models.
Yifan Jiang, Men Xia, Zhe Wang, Penggang Zheng, Yi Chen, and Tao Wang
Atmos. Chem. Phys., 23, 14813–14828, https://doi.org/10.5194/acp-23-14813-2023, https://doi.org/10.5194/acp-23-14813-2023, 2023
Short summary
Short summary
This study provides the first estimate of high rates of formic acid (HCOOH) production from the photochemical aging of real ambient particles and demonstrates the potential importance of this pathway in the formation of HCOOH under ambient conditions. Incorporating this pathway significantly improved the performance of a widely used chemical model. Our solution irradiation experiments demonstrated the importance of nitrate photolysis in HCOOH production via the production of oxidants.
Jianing Dai, Guy P. Brasseur, Mihalis Vrekoussis, Maria Kanakidou, Kun Qu, Yijuan Zhang, Hongliang Zhang, and Tao Wang
Atmos. Chem. Phys., 23, 14127–14158, https://doi.org/10.5194/acp-23-14127-2023, https://doi.org/10.5194/acp-23-14127-2023, 2023
Short summary
Short summary
In this study, we used a regional chemical transport model to characterize the different parameters of atmospheric oxidative capacity in recent chemical environments in China. These parameters include the production and destruction rates of ozone and other oxidants, the ozone production efficiency, the OH reactivity, and the length of the reaction chain responsible for the formation of ozone and ROx. They are also affected by the aerosol burden in the atmosphere.
Qianqian Gao, Shengqiang Zhu, Kaili Zhou, Jinghao Zhai, Shaodong Chen, Qihuang Wang, Shurong Wang, Jin Han, Xiaohui Lu, Hong Chen, Liwu Zhang, Lin Wang, Zimeng Wang, Xin Yang, Qi Ying, Hongliang Zhang, Jianmin Chen, and Xiaofei Wang
Atmos. Chem. Phys., 23, 13049–13060, https://doi.org/10.5194/acp-23-13049-2023, https://doi.org/10.5194/acp-23-13049-2023, 2023
Short summary
Short summary
Dust is a major source of atmospheric aerosols. Its chemical composition is often assumed to be similar to the parent soil. However, this assumption has not been rigorously verified. Dust aerosols are mainly generated by wind erosion, which may have some chemical selectivity. Mn, Cd and Pb were found to be highly enriched in fine-dust (PM2.5) aerosols. In addition, estimation of heavy metal emissions from dust generation by air quality models may have errors without using proper dust profiles.
Xiaodong Xie, Jianlin Hu, Momei Qin, Song Guo, Min Hu, Dongsheng Ji, Hongli Wang, Shengrong Lou, Cheng Huang, Chong Liu, Hongliang Zhang, Qi Ying, Hong Liao, and Yuanhang Zhang
Atmos. Chem. Phys., 23, 10563–10578, https://doi.org/10.5194/acp-23-10563-2023, https://doi.org/10.5194/acp-23-10563-2023, 2023
Short summary
Short summary
The atmospheric age of particles reflects how long particles have been formed and suspended in the atmosphere, which is closely associated with the evolution processes of particles. An analysis of the atmospheric age of PM2.5 provides a unique perspective on the evolution processes of different PM2.5 components. The results also shed lights on how to design effective emission control actions under unfavorable meteorological conditions.
Zhouxing Zou, Qianjie Chen, Men Xia, Qi Yuan, Yi Chen, Yanan Wang, Enyu Xiong, Zhe Wang, and Tao Wang
Atmos. Chem. Phys., 23, 7057–7074, https://doi.org/10.5194/acp-23-7057-2023, https://doi.org/10.5194/acp-23-7057-2023, 2023
Short summary
Short summary
We present OH observation and model simulation results at a coastal site in Hong Kong. The model predicted the OH concentration under high-NOx well but overpredicted it under low-NOx conditions. This implies an insufficient understanding of OH chemistry under low-NOx conditions. We show evidence of missing OH sinks as a possible cause of the overprediction.
Yishuo Guo, Chenjuan Deng, Aino Ovaska, Feixue Zheng, Chenjie Hua, Junlei Zhan, Yiran Li, Jin Wu, Zongcheng Wang, Jiali Xie, Ying Zhang, Tingyu Liu, Yusheng Zhang, Boying Song, Wei Ma, Yongchun Liu, Chao Yan, Jingkun Jiang, Veli-Matti Kerminen, Men Xia, Tuomo Nieminen, Wei Du, Tom Kokkonen, and Markku Kulmala
Atmos. Chem. Phys., 23, 6663–6690, https://doi.org/10.5194/acp-23-6663-2023, https://doi.org/10.5194/acp-23-6663-2023, 2023
Short summary
Short summary
Using the comprehensive datasets, we investigated the long-term variations of air pollutants during winter in Beijing from 2019 to 2022 and analyzed the characteristics of atmospheric pollution cocktail during different short-term special events (e.g., Beijing Winter Olympics, COVID lockdown and Chinese New Year) associated with substantial emission reductions. Our results are useful in planning more targeted and sustainable long-term pollution control plans.
Jinlong Ma, Shengqiang Zhu, Siyu Wang, Peng Wang, Jianmin Chen, and Hongliang Zhang
Atmos. Chem. Phys., 23, 4311–4325, https://doi.org/10.5194/acp-23-4311-2023, https://doi.org/10.5194/acp-23-4311-2023, 2023
Short summary
Short summary
An updated version of the CMAQ model with biogenic volatile organic compound (BVOC) emissions from MEGAN was applied to study the impacts of different land cover inputs on O3 and secondary organic aerosol (SOA) in China. The estimated BVOC emissions ranged from 25.42 to 37.39 Tg using different leaf area index (LAI) and land cover (LC) inputs. Those differences further induced differences of 4.8–6.9 ppb in O3 concentrations and differences of 5.3–8.4 µg m−3 in SOA concentrations in China.
Peng Wang, Ruhan Zhang, Shida Sun, Meng Gao, Bo Zheng, Dan Zhang, Yanli Zhang, Gregory R. Carmichael, and Hongliang Zhang
Atmos. Chem. Phys., 23, 2983–2996, https://doi.org/10.5194/acp-23-2983-2023, https://doi.org/10.5194/acp-23-2983-2023, 2023
Short summary
Short summary
In China, the number of vehicles has jumped significantly in the last decade. This caused severe traffic congestion and aggravated air pollution. In this study, we developed a new temporal allocation approach to quantify the impacts of traffic congestion. We found that traffic congestion worsens air quality and the health burden across China, especially in the urban clusters. More effective and comprehensive vehicle emission control policies should be implemented to improve air quality in China.
Men Xia, Xiang Peng, Weihao Wang, Chuan Yu, Zhe Wang, Yee Jun Tham, Jianmin Chen, Hui Chen, Yujing Mu, Chenglong Zhang, Pengfei Liu, Likun Xue, Xinfeng Wang, Jian Gao, Hong Li, and Tao Wang
Atmos. Chem. Phys., 21, 15985–16000, https://doi.org/10.5194/acp-21-15985-2021, https://doi.org/10.5194/acp-21-15985-2021, 2021
Short summary
Short summary
ClNO2 is an important precursor of chlorine radical that affects photochemistry. However, its production and impact are not well understood. Our study presents field observations of ClNO2 at three sites in northern China. These observations provide new insights into nighttime processes that produce ClNO2 and the significant impact of ClNO2 on secondary pollutions during daytime. The results improve the understanding of photochemical pollution in the lower part of the atmosphere.
Peng Wang, Juanyong Shen, Men Xia, Shida Sun, Yanli Zhang, Hongliang Zhang, and Xinming Wang
Atmos. Chem. Phys., 21, 10347–10356, https://doi.org/10.5194/acp-21-10347-2021, https://doi.org/10.5194/acp-21-10347-2021, 2021
Short summary
Short summary
Ozone (O3) pollution has received extensive attention due to worsening air quality and rising health risks. The Chinese National Day holiday (CNDH), which is associated with intensive commercial and tourist activities, serves as a valuable experiment to evaluate the O3 response during the holiday. We find sharply increasing trends of observed O3 concentrations throughout China during the CNDH, leading to 33 % additional total daily deaths.
Lian Zong, Yuanjian Yang, Meng Gao, Hong Wang, Peng Wang, Hongliang Zhang, Linlin Wang, Guicai Ning, Chao Liu, Yubin Li, and Zhiqiu Gao
Atmos. Chem. Phys., 21, 9105–9124, https://doi.org/10.5194/acp-21-9105-2021, https://doi.org/10.5194/acp-21-9105-2021, 2021
Short summary
Short summary
In recent years, summer O3 pollution over eastern China has become more serious, and it is even the case that surface O3 and PM2.5 pollution can co-occur. However, the synoptic weather pattern (SWP) related to this compound pollution remains unclear. Regional PM2.5 and O3 compound pollution is characterized by various SWPs with different dominant factors. Our findings provide insights into the regional co-occurring high PM2.5 and O3 levels via the effects of certain meteorological factors.
Jinlong Ma, Juanyong Shen, Peng Wang, Shengqiang Zhu, Yu Wang, Pengfei Wang, Gehui Wang, Jianmin Chen, and Hongliang Zhang
Atmos. Chem. Phys., 21, 7343–7355, https://doi.org/10.5194/acp-21-7343-2021, https://doi.org/10.5194/acp-21-7343-2021, 2021
Short summary
Short summary
Due to the reduced anthropogenic emissions during the COVID-19 lockdown, mainly from the transportation and industrial sectors, PM2.5 decreased significantly in the whole Yangtze River Delta (YRD) and its major cities. However, the contributions and relative importance of different source sectors and regions changed differently, indicating that control strategies should be adjusted accordingly for further pollution control.
Junjun Deng, Hao Guo, Hongliang Zhang, Jialei Zhu, Xin Wang, and Pingqing Fu
Atmos. Chem. Phys., 20, 14419–14435, https://doi.org/10.5194/acp-20-14419-2020, https://doi.org/10.5194/acp-20-14419-2020, 2020
Short summary
Short summary
One-year source apportionment of BC aerosols in a coastal city in China was conducted with the light-absorption observation-based method and source-oriented model. Source contributions identified by the two source apportionment methods were compared. Temporal variability, potential sources and transport pathways of BC from fossil fuel and biomass burning were characterized. Significant influence of biomass burning in North and East–Central China on BC in the region was highlighted.
Zhihao Shi, Lin Huang, Jingyi Li, Qi Ying, Hongliang Zhang, and Jianlin Hu
Atmos. Chem. Phys., 20, 13455–13466, https://doi.org/10.5194/acp-20-13455-2020, https://doi.org/10.5194/acp-20-13455-2020, 2020
Short summary
Short summary
Meteorological conditions play important roles in the formation of O3 and PM2.5 pollution in China. O3 is most sensitive to temperature and the sensitivity is dependent on the O3 chemistry formation or loss regime. PM2.5 is negatively sensitive to temperature, wind speed, and planetary boundary layer height and positively sensitive to humidity. The results imply that air quality in certain regions of China is sensitive to climate changes.
Cited articles
Abdi, B.: The Economic Times, Coronavirus impact: Within ten days, 26 per cent fall in India's energy consumption, available at: https://energy.economictimes.indiatimes.com/news/power/, last access: 27 August 2020.
Ali, K., Inamdar, S. R., Beig, G., Ghude, S., and Peshin, S.: Surface ozone
scenario at Pune and Delhi during the decade of 1990s, J. Earth
Syst. Sci., 121, 373–383, https://doi.org/10.1007/s12040-012-0170-1,
2012.
Anderson, R. M., Heesterbeek, H., Klinkenberg, D., and Hollingsworth, T. D.:
How will country-based mitigation measures influence the course of the
COVID-19 epidemic?, Lancet, 395, 931–934,
https://doi.org/10.1016/s0140-6736(20)30567-5, 2020.
Apituley, A., Pedergnana, M., Sneep, M., Pepijn Veefkind, J., Loyola, D., Landgraf, J., and Borsdorff, T.: Sentinel-5 Precursor/TROPOMI Level 2 Product User Manual Carbon Monoxide, SRON-S5P-LEV2-MA-002, avaliable at: http://www.tropomi.eu/sites/default/files/files/Sentinel-5P-Level-2-Product-User-Manual-CarbonMonoxide_v1.00.02_20180613.pdf (last access: 7 February 2021), 2018.
Balakrishnan, K., Dey, S., Gupta, T., Dhaliwal, R. S., Brauer, M., Cohen, A.
J., Stanaway, J. D., Beig, G., Joshi, T. K., Aggarwal, A. N., Sabde, Y.,
Sadhu, H., Frostad, J., Causey, K., Godwin, W., Shukla, D. K., Kumar, G. A.,
Varghese, C. M., Muraleedharan, P., Agrawal, A., Anjana, R. M., Bhansali,
A., Bhardwaj, D., Burkart, K., Cercy, K., Chakma, J. K., Chowdhury, S.,
Christopher, D. J., Dutta, E., Furtado, M., Ghosh, S., Ghoshal, A. G.,
Glenn, S. D., Guleria, R., Gupta, R., Jeemon, P., Kant, R., Kant, S., Kaur,
T., Koul, P. A., Krish, V., Krishna, B., Larson, S. L., Madhipatla, K.,
Mahesh, P. A., Mohan, V., Mukhopadhyay, S., Mutreja, P., Naik, N., Nair, S.,
Nguyen, G., Odell, C. M., Pandian, J. D., Prabhakaran, D., Prabhakaran, P.,
Roy, A., Salvi, S., Sambandam, S., Saraf, D., Sharma, M., Shrivastava, A.,
Singh, V., Tandon, N., Thomas, N. J., Torre, A., Xavier, D., Yadav, G.,
Singh, S., Shekhar, C., Vos, T., Dandona, R., Reddy, K. S., Lim, S. S.,
Murray, C. J. L., Venkatesh, S., and Dandona, L.: The impact of air
pollution on deaths, disease burden, and life expectancy across the states
of India: the Global Burden of Disease Study 2017, Lancet Planet. Health, 3, e26–e39, https://doi.org/10.1016/S2542-5196(18)30261-4, 2019.
Banerjee, T., Kumar, M., Mall, R. K., and Singh, R. S.: Airing 'clean air'
in Clean India Mission, Environ. Sci. Pollut. Res., 24,
6399–6413, https://doi.org/10.1007/s11356-016-8264-y, 2017.
Bao, R. and Zhang, A.: Does lockdown reduce air pollution? Evidence from 44
cities in northern China, Sci. Total Environ., 731, 139052,
https://doi.org/10.1016/j.scitotenv.2020.139052, 2020.
Beig, G., Chate, D. M., Ghude, S. D., Mahajan, A. S., Srinivas, R., Ali, K.,
Sahu, S. K., Parkhi, N., Surendran, D., and Trimbake, H. R.: Quantifying the
effect of air quality control measures during the 2010 Commonwealth Games at
Delhi, India, Atmos. Environ., 80, 455–463,
https://doi.org/10.1016/j.atmosenv.2013.08.012, 2013.
Binkowski, F. S. and Roselle, S. J.: Models-3 Community Multiscale Air Quality (CMAQ) model aerosol component 1. Model description, J. Geophys. Res.-Atmos., 108, 4183, https://doi.org/10.1029/2001JD001409, 2003.
Bujin, B., Joshua, S. A., Dylan, B. M., Allen, R., Kelley, C. W., and Julian, D. M.: PM2.5 and Ozone Air Pollution Levels Have Not Dropped Consistently Across the US Following Societal Covid Response, ChemRxiv [preprint], https://doi.org/10.26434/chemrxiv.12275603.v3, 2020.
Byun, D. and Schere, K. L.: Review of the governing equations,
computational algorithms, and other components of the models-3 Community
Multiscale Air Quality (CMAQ) modeling system, Appl. Mech. Rev.,
59, 51–77, https://doi.org/10.1115/1.2128636, 2006.
Carter, W. P. L.: SAPRC Atmospheric Chemical Mechanisms and VOC Reactivity
Scales, available at: https://intra.engr.ucr.edu/~carter/SAPRC/ (last access: 26 August 2020), 2011.
Chauhan, A. and Singh, R. P.: Decline in PM2.5 concentrations over
major cities around the world associated with COVID-19, Environ.
Res., 187, 109634, https://doi.org/10.1016/j.envres.2020.109634, 2020.
Chen, Z., Zhuang, Y., Xie, X., Chen, D., Cheng, N., Yang, L., and Li, R.:
Understanding long-term variations of meteorological influences on ground
ozone concentrations in Beijing During 2006–2016, Environ. Pollut.,
245, 29–37, https://doi.org/10.1016/j.envpol.2018.10.117, 2019.
Chintalapudi, N., Battineni, G., and Amenta, F.: COVID-19 virus outbreak
forecasting of registered and recovered cases after sixty day lockdown in
Italy: A data driven model approach, J. Microbiol. Immunol., 53, 396–403, https://doi.org/10.1016/j.jmii.2020.04.004, 2020.
Collivignarelli, M. C., Abba, A., Bertanza, G., Pedrazzani, R., Ricciardi,
P., and Carnevale Miino, M.: Lockdown for CoViD-2019 in Milan: What are the
effects on air quality?, Sci. Total Environ., 732, 139280,
https://doi.org/10.1016/j.scitotenv.2020.139280, 2020.
Dantas, G., Siciliano, B., Franca, B. B., da Silva, C. M., and Arbilla, G.:
The impact of COVID-19 partial lockdown on the air quality of the city of
Rio de Janeiro, Brazil, Sci. Total Environ., 729, 139085,
https://doi.org/10.1016/j.scitotenv.2020.139085, 2020.
Das, M., Das, A., Sarkar, R., Saha, S., and Mandal, A.: Examining the impact
of lockdown (due to COVID-19) on ambient aerosols (PM2.5): A study on
Indo-Gangetic Plain (IGP) Cities, India, Stoch. Env. Res. Risk A., 16, 1–17, https://doi.org/10.1007/s00477-020-01905-x, 2020.
David, L. M., Ravishankara, A. R., Kodros, J. K., Pierce, J. R.,
Venkataraman, C., and Sadavarte, P.: Premature Mortality Due to PM2.5
Over India: Effect of Atmospheric Transport and Anthropogenic Emissions,
Geohealth, 3, 2–10, https://doi.org/10.1029/2018GH000169, 2019.
Dominutti, P., Nogueira, T., Fornaro, A., and Borbon, A.: One decade of VOCs
measurements in São Paulo megacity: Composition, variability, and
emission evaluation in a biofuel usage context, Sci. Total
Environ., 738, 139790, https://doi.org/10.1016/j.scitotenv.2020.139790,
2020.
Ehrlich, H., McKenney, M., and Elkbuli, A.: Protecting our healthcare
workers during the COVID-19 pandemic, Am. J. Emerg.
Med., 38, 1527–1528, https://doi.org/10.1016/j.ajem.2020.04.024, 2020.
Emery, C., Tai, E., and Yarwood, G.: Enhanced Meteorological Modeling and Performance Evaluation for Two texas Episodes, Report to the Texas Natural Resources Conservation Commission, Prepared by ENVIRON, International Corp., Novato, CA, available at: http://www.tceq.state.tx.us/assets/public/implementation/air/am/contracts/reports/mm/EnhancedMetModelingAndPerformanceEvaluation.pdf (last access: 27 August 2020), 2001.
EPA: Guidance on the Use of Models and Other Analyses in Attainment Demonstrations for the 8-hour Ozone, NAAQSRep, available at: https://nepis.epa.gov/Exe/ZyPDF.cgi/P1006FPU.PDF?Dockey=P1006FPU.PDF, (last access: 27 August 2020), 2005.
EPA: Guidance on the use of models and other analyses for demonstrating attainment of air quality goals for ozone, PM2.5, and regional haze, US Environmental Protection Agency, Office of Air Quality Planning and Standards, available at: https://nepis.epa.gov/Exe/ZyPDF.cgi/P1009OL1.PDF?Dockey=P1009OL1.PDF (last access: 27 August 2020), 2007.
Eskes, K.-U. E., Lambert, J.-C., Loyola, D., Veefkind, J. P., Dehn, A., and Zehner, C.: S5P Mission Performance Centre Nitrogen Dioxide [L2_NO2_] Readme, available at: https://sentinel.esa.int/documents/247904/3541451/Sentinel-5P-Nitrogen-Dioxide-Level-2-Product-Readme-File (last access: 7 February 2021), 2020.
ET Bureau: The Economic Times, Move only essential items: Transport body to members, available at: https://economictimes.indiatimes.com/industry/transportation/shipping-/-transport/move-only-essential-items-transport-body-to-members/articleshow/75016374.cms?from=mdr, last access: 27 August 2020.
Feng, T., Zhao, S., Bei, N., Wu, J., Liu, S., Li, X., Liu, L., Qian, Y., Yang, Q., Wang, Y., Zhou, W., Cao, J., and Li, G.: Secondary organic aerosol enhanced by increasing atmospheric oxidizing capacity in Beijing–Tianjin–Hebei (BTH), China, Atmos. Chem. Phys., 19, 7429–7443, https://doi.org/10.5194/acp-19-7429-2019, 2019.
Fountoukis, C. and Nenes, A.: ISORROPIA II: a computationally efficient thermodynamic equilibrium model for K+–Ca2+–Mg2+–NH
Garaga, R., Gokhale, S., and Kota, S. H.: Source apportionment of
size-segregated atmospheric particles and the influence of particles
deposition in the human respiratory tract in rural and urban locations of
north-east India, Chemosphere, 255, 126980,
https://doi.org/10.1016/j.chemosphere.2020.126980, 2020.
Gautam, S.: The Influence of COVID-19 on Air Quality in India: A Boon or
Inutile, B. Environ. Contam. Tox., 104, 724–726, https://doi.org/10.1007/s00128-020-02877-y, 2020.
Gawhane, R. D., Rao, P. S. P., Budhavant, K. B., Waghmare, V., Meshram, D.
C., and Safai, P. D.: Seasonal variation of chemical composition and source
apportionment of PM2.5 in Pune, India, Environ. Sci.
Pollut. Res., 24, 21065–21072,
https://doi.org/10.1007/s11356-017-9761-3, 2017.
Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492, https://doi.org/10.5194/gmd-5-1471-2012, 2012.
Guo, H., Kota, S. H., Sahu, S. K., Hu, J., Ying, Q., Gao, A., and Zhang, H.:
Source apportionment of PM2.5 in North India using source-oriented air
quality models, Environ. Pollut., 231, 426–436,
https://doi.org/10.1016/j.envpol.2017.08.016, 2017.
Gurjar, B. R., Ravindra, K., and Nagpure, A. S.: Air pollution trends over
Indian megacities and their local-to-global implications, Atmos.
Environ., 142, 475–495, https://doi.org/10.1016/j.atmosenv.2016.06.030,
2016.
Guttikunda, S. K., Nishadh, K. A., Gota, S., Singh, P., Chanda, A., Jawahar,
P., and Asundi, J.: Air quality, emissions, and source contributions
analysis for the Greater Bengaluru region of India, Atmos. Pollut.
Res., 10, 941–953, https://doi.org/10.1016/j.apr.2019.01.002, 2019.
Hu, J., Wu, L., Zheng, B., Zhang, Q., He, K., Chang, Q., Li, X., Yang, F.,
Ying, Q., and Zhang, H.: Source contributions and regional transport of
primary particulate matter in China, Environ. Pollut., 207, 31–42,
https://doi.org/10.1016/j.envpol.2015.08.037, 2015.
Hu, J., Chen, J., Ying, Q., and Zhang, H.: One-year simulation of ozone and particulate matter in China using WRF/CMAQ modeling system, Atmos. Chem. Phys., 16, 10333–10350, https://doi.org/10.5194/acp-16-10333-2016, 2016.
Isaifan, R. J.: The dramatic impact of Coronavirus outbreak on air quality:
Has it saved as much as it has killed so far?, Global J.
Environ. Sci. Manage., 6, 275–288,
https://doi.org/10.22034/gjesm.2020.03.01, 2020.
Jain, S., Sharma, S. K., Vijayan, N., and Mandal, T. K.: Seasonal
characteristics of aerosols (PM2.5 and PM10) and their source
apportionment using PMF: A four year study over Delhi, India, Environ.
Pollut., 262, 114337, https://doi.org/10.1016/j.envpol.2020.114337, 2020.
Kabiraj, S. and Gavli, N. V.: Impact of SARS-CoV-2 Pandemic Lockdown on Air
Quality Using Satellite Imagery with Ground Station Monitoring Data in Most
Polluted City Kolkata, India, Aerosol Sci. Eng., 4, 320–330,
10.1007/s41810-020-00077-z, 2020.
Karambelas, A., Holloway, T., Kiesewetter, G., and Heyes, C.: Constraining
the uncertainty in emissions over India with a regional air quality model
evaluation, Atmos. Environ., 174, 194–203,
https://doi.org/10.1016/j.atmosenv.2017.11.052, 2018.
Kitayama, K., Morino, Y., Yamaji, K., and Chatani, S.: Uncertainties in
O3 concentrations simulated by CMAQ over Japan using four chemical
mechanisms, Atmos. Environ., 198, 448–462,
https://doi.org/10.1016/j.atmosenv.2018.11.003, 2019.
Kota, S. H., Guo, H., Myllyvirta, L., Hu, J., Sahu, S. K., Garaga, R., Ying,
Q., Gao, A., Dahiya, S., Wang, Y., and Zhang, H.: Year-long simulation of
gaseous and particulate air pollutants in India, Atmos. Environ.,
180, 244–255, https://doi.org/10.1016/j.atmosenv.2018.03.003, 2018.
Kroll, J. H., Heald, C. L., Cappa, C. D., Farmer, D. K., Fry, J. L., Murphy,
J. G., and Steiner, A. L.: The complex chemical effects of COVID-19
shutdowns on air quality, Nat. Chem., 12, 777–779,
https://doi.org/10.1038/s41557-020-0535-z, 2020.
Kumar, P., Hama, S., Omidvarborna, H., Sharma, A., Sahani, J., Abhijith, K.
V., Debele, S. E., Zavala-Reyes, J. C., Barwise, Y., and Tiwari, A.:
Temporary reduction in fine particulate matter due to `anthropogenic
emissions switch-off' during COVID-19 lockdown in Indian cities, Sustain.
Cities Soc., 62, 102382, https://doi.org/10.1016/j.scs.2020.102382,
2020.
Kumar, S.: Effect of meteorological parameters on spread of COVID-19 in
India and air quality during lockdown, Sci. Total Environ.,
745, 141021, https://doi.org/10.1016/j.scitotenv.2020.141021, 2020.
Kumari, P., and Toshniwal, D.: Impact of lockdown measures during COVID-19 on air quality – A case study of India, Int. J. Environ. Health Res., https://doi.org/10.1080/09603123.2020.1778646, in press, 2020.
Lal, S., Venkataramani, S., Naja, M., Kuniyal, J. C., Mandal, T. K., Bhuyan,
P. K., Kumari, K. M., Tripathi, S. N., Sarkar, U., Das, T., Swamy, Y. V.,
Gopal, K. R., Gadhavi, H., and Kumar, M. K. S.: Loss of crop yields in India
due to surface ozone: an estimation based on a network of observations,
Environ. Sci. Pollut. Res., 24, 20972–20981,
https://doi.org/10.1007/s11356-017-9729-3, 2017.
Le, T., Wang, Y., Liu, L., Yang, J., Yung, Y. L., Li, G., and Seinfeld, J. H.: Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China, Science, 369, 702–706, https://doi.org/10.1126/science.abb7431, 2020.
Mahajan, A. S., De Smedt, I., Biswas, M. S., Ghude, S., Fadnavis, S., Roy,
C., and van Roozendael, M.: Inter-annual variations in satellite
observations of nitrogen dioxide and formaldehyde over India, Atmos.
Environ., 116, 194–201, https://doi.org/10.1016/j.atmosenv.2015.06.004,
2015.
Mahato, S., Pal, S., and Ghosh, K. G.: Effect of lockdown amid COVID-19
pandemic on air quality of the megacity Delhi, India, Sci. Total
Environ., 730, 139086, https://doi.org/10.1016/j.scitotenv.2020.139086,
2020.
Mohan, M. and Gupta, M.: Sensitivity of PBL parameterizations on PM10
and ozone simulation using chemical transport model WRF-Chem over a
sub-tropical urban airshed in India, Atmos. Environ., 185, 53–63,
https://doi.org/10.1016/j.atmosenv.2018.04.054, 2018.
Mor, S., Kumar, S., Singh, T., Dogra, S., Pandey, V., and Ravindra, K.:
Impact of COVID-19 lockdown on air quality in Chandigarh, India:
Understanding the emission sources during controlled anthropogenic
activities, Chemosphere, 263, 127978,
https://doi.org/10.1016/j.chemosphere.2020.127978, 2021.
Mukherjee, K.: COVID-19 and lockdown: Insights from Mumbai, Indian J. Pub. Health, 64, 168–171,
https://doi.org/10.4103/ijph.IJPH_508_20,
2020.
Nakada, L. Y. K. and Urban, R. C.: COVID-19 pandemic: Impacts on the air
quality during the partial lockdown in Sao Paulo state, Brazil, Sci.
Total Environ., 730, 139087,
https://doi.org/10.1016/j.scitotenv.2020.139087, 2020.
Oksanen, E., Pandey, V., Pandey, A. K., Keski-Saari, S., Kontunen-Soppela,
S., and Sharma, C.: Impacts of increasing ozone on Indian plants,
Environ. Pollut., 177, 189–200,
https://doi.org/10.1016/j.envpol.2013.02.010, 2013.
Otmani, A., Benchrif, A., Tahri, M., Bounakhla, M., Chakir, E. M., El Bouch,
M., and Krombi, M.: Impact of Covid-19 lockdown on PM10, SO2 and
NO2 concentrations in Sale City (Morocco), Sci. Total
Environ., 735, 139541, https://doi.org/10.1016/j.scitotenv.2020.139541,
2020.
Pai, C., Bhaskar, A., and Rawoot, V.: Investigating the dynamics of COVID-19
pandemic in India under lockdown, Chaos Soliton. Fract., 138,
109988, https://doi.org/10.1016/j.chaos.2020.109988, 2020.
Palmer, P. I., Jacob, D. J., Fiore, A. M., Martin, R. V., Chance, K., and Kurosu, T. P.: Mapping isoprene emissions over North America using formaldehyde column observations from space, J. Geophys. Res.-Atmos., 108, 4180, https://doi.org/10.1029/2002JD002153, 2003.
Purohit, P., Amann, M., Kiesewetter, G., Rafaj, P., Chaturvedi, V.,
Dholakia, H. H., Koti, P. N., Klimont, Z., Borken-Kleefeld, J.,
Gomez-Sanabria, A., Schopp, W., and Sander, R.: Mitigation pathways towards
national ambient air quality standards in India, Environ. Int.,
133, 105147, https://doi.org/10.1016/j.envint.2019.105147, 2019.
Reuters, S. V.: UPDATE 1 – India's March Electricity Usage Falls 9.2 % as Lock-Down Bites, available at: https://uk.reuters.com/article/india-electricity-supply/update-1-indias-march-electricity-usage-falls-9-2-as-lockdown, last access: 26 August 2020.
Sahu, S. K., Sharma, S., Zhang, H., Chejarla, V., Guo, H., Hu, J., Ying, Q.,
Xing, J., and Kota, S. H.: Estimating ground level PM2.5 concentrations
and associated health risk in India using satellite based AOD and WRF
predicted meteorological parameters, Chemosphere, 255,
126969, https://doi.org/10.1016/j.chemosphere.2020.126969, 2020.
Schnell, J. L., Naik, V., Horowitz, L. W., Paulot, F., Mao, J., Ginoux, P., Zhao, M., and Ram, K.: Exploring the relationship between surface PM2.5 and meteorology in Northern India, Atmos. Chem. Phys., 18, 10157–10175, https://doi.org/10.5194/acp-18-10157-2018, 2018.
Selvam, S., Muthukumar, P., Venkatramanan, S., Roy, P. D., Manikanda
Bharath, K., and Jesuraja, K.: SARS-CoV-2 pandemic lockdown: Effects on air
quality in the industrialized Gujarat state of India, Sci. Total
Environ., 737, 140391, https://doi.org/10.1016/j.scitotenv.2020.140391,
2020.
Sharma, S., Chatani, S., Mahtta, R., Goel, A., and Kumar, A.: Sensitivity
analysis of ground level ozone in India using WRF-CMAQ models, Atmos.
Environ., 131, 29–40, https://doi.org/10.1016/j.atmosenv.2016.01.036,
2016.
Sharma, S., Zhang, M., Anshika, Gao, J., Zhang, H., and Kota, S. H.: Effect
of restricted emissions during COVID-19 on air quality in India, Sci. Total Environ., 728, 138878,
https://doi.org/10.1016/j.scitotenv.2020.138878, 2020.
Shehzad, K., Sarfraz, M., and Shah, S. G. M.: The impact of COVID-19 as a
necessary evil on air pollution in India during the lockdown, Environ.
Pollut., 266, 115080, https://doi.org/10.1016/j.envpol.2020.115080, 2020.
Sicard, P., De Marco, A., Agathokleous, E., Feng, Z., Xu, X., Paoletti, E.,
Rodriguez, J. J. D., and Calatayud, V.: Amplified ozone pollution in cities
during the COVID-19 lockdown, Sci. Total Environ., 735, 139542,
https://doi.org/10.1016/j.scitotenv.2020.139542, 2020.
Sillman, S.: The use of NOy, H2O2, and HNO3 as
indicators for ozone-NOx-hydrocarbon sensitivity in urban locations,
J. Geophys. Res.-Atmos., 100, 14175–14188,
https://doi.org/10.1029/94JD02953, 1995.
Sillman, S. and He, D.: Some theoretical results concerning
O3-NOx-VOC chemistry and NOx-VOC indicators, J.
Geophys. Res.-Atmos., 107, ACH 26-21–ACH 26-15,
https://doi.org/10.1029/2001JD001123, 2002.
Srivastava, S., Kumar, A., Bauddh, K., Gautam, A. S., and Kumar, S.: 21-Day
Lockdown in India Dramatically Reduced Air Pollution Indices in Lucknow and
New Delhi, India, B. Environ. Contam. Tox.,
105, 9–17, https://doi.org/10.1007/s00128-020-02895-w, 2020.
Steiner, A. L., Cohen, R. C., Harley, R. A., Tonse, S., Millet, D. B., Schade, G. W., and Goldstein, A. H.: VOC reactivity in central California: comparing an air quality model to ground-based measurements, Atmos. Chem. Phys., 8, 351–368, https://doi.org/10.5194/acp-8-351-2008, 2008.
Wang, D., Hu, J., Xu, Y., Lv, D., Xie, X., Kleeman, M., Xing, J., Zhang, H.,
and Ying, Q.: Source contributions to primary and secondary inorganic
particulate matter during a severe wintertime PM2.5 pollution episode
in Xi'an, China, Atmos. Environ., 97, 182–194,
https://doi.org/10.1016/j.atmosenv.2014.08.020, 2014.
Wang, P., Chen, K., Zhu, S., Wang, P., and Zhang, H.: Severe air pollution
events not avoided by reduced anthropogenic activities during COVID-19
outbreak, Resour. Conserv. Recy., 158, 104814,
https://doi.org/10.1016/j.resconrec.2020.104814, 2020.
WHO: Global urban ambient air pollution database (update 2016), available at: http://www.who.int/phe/health_topics/outdoorair/databases/cities/en/, (last access: 27 August 2020), 2016.
WHO: WHO Global Ambient Air Quality Database (Update 2018), available at: http://www.who.int/airpollution/data/cities/en/ (last access: 27 August 2020), 2018.
Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., and Soja, A. J.: The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning, Geosci. Model Dev., 4, 625–641, https://doi.org/10.5194/gmd-4-625-2011, 2011.
Zhang, H., Wang, Y., Hu, J., Ying, Q., and Hu, X. M.: Relationships between
meteorological parameters and criteria air pollutants in three megacities in
China, Environ. Res., 140, 242–254,
https://doi.org/10.1016/j.envres.2015.04.004, 2015.
Zhang, Q., Shao, M., Li, Y., Lu, S. H., Yuan, B., and Chen, W. T.: Increase
of ambient formaldehyde in Beijing and its implication for VOC reactivity,
Chinese Chem. Lett., 23, 1059–1062,
https://doi.org/10.1016/j.cclet.2012.06.015, 2012.
Zhao, B., Wu, W., Wang, S., Xing, J., Chang, X., Liou, K.-N., Jiang, J. H., Gu, Y., Jang, C., Fu, J. S., Zhu, Y., Wang, J., Lin, Y., and Hao, J.: A modeling study of the nonlinear response of fine particles to air pollutant emissions in the Beijing–Tianjin–Hebei region, Atmos. Chem. Phys., 17, 12031–12050, https://doi.org/10.5194/acp-17-12031-2017, 2017.
Short summary
We studied changes in air quality in India induced by the COVID-19 lockdown through both surface observations and the CMAQ model. Our results show that emission reductions improved the air quality across India during the lockdown. On average, the levels of PM2.5 and O3 decreased by 28 % and 15 %, indicating positive effects of lockdown measures. We suggest that more stringent and localized emission control strategies should be implemented in India to mitigate air pollutions.
We studied changes in air quality in India induced by the COVID-19 lockdown through both surface...
Altmetrics
Final-revised paper
Preprint