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Mushtaq Z, Bangotra P, Gautam AS, Sharma M, Suman, Gautam S, Singh K, Kumar Y, Jain P. Satellite or ground-based measurements for air pollutants (PM 2.5, PM 10, SO 2, NO 2, O 3) data and their health hazards: which is most accurate and why? ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:342. [PMID: 38438750 DOI: 10.1007/s10661-024-12462-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 02/17/2024] [Indexed: 03/06/2024]
Abstract
Air pollution is growing at alarming rates on regional and global levels, with significant consequences for human health, ecosystems, and change in climatic conditions. The present 12 weeks (4 October 2021, to 26 December 2021) study revealed the different ambient air quality parameters, i.e., PM2.5, PM10, SO2, NO2, and O3 over four different sampling stations of Delhi-NCR region (Dwarka, Knowledge park III, Sector 125, and Vivek Vihar), India, by using satellite remote sensing data (MERRA-2, OMI, and Aura Satellite) and different ground-based instruments. The ground-based observation revealed the mean concentration of PM2.5 in Dwarka, Knowledge park III, Sector 125, and Vivek Vihar as 279 µg m-3, 274 µg m-3, 294 µg m-3, and 365 µg m-3, respectively. The ground-based instrumental concentration of PM2.5 was greater than that of satellite observations, while as for SO2 and NO2, the mean concentration of satellite-based monitoring was higher as compared to other contaminants. Negative and positive correlations were observed among particulate matter, trace gases, and various meteorological parameters. The wind direction proved to be one of the prominent parameter to alter the variation of these pollutants. The current study provides a perception into an observable behavior of particulate matter, trace gases, their variation with meteorological parameters, their health hazards, and the gap between the measurements of satellite remote sensing and ground-based measurements.
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Affiliation(s)
- Zainab Mushtaq
- Atmospheric Research Laboratory, Department of Environmental Sciences, SSBSR, Sharda University, Greater Noida, India
| | - Pargin Bangotra
- Department of Physics, Netaji Subhas University of Technology, Dwarka, New Delhi, 110078, India.
| | - Alok Sagar Gautam
- Department of Physics, Hemvati Nandan Bahuguna Garhwal University, Srinagar, Uttarakhand, India.
| | - Manish Sharma
- School of Science and Technology, Himgiri Zee University, Dehradun, Uttarakhand, India
| | - Suman
- Atmospheric Research Laboratory, Department of Environmental Sciences, SSBSR, Sharda University, Greater Noida, India
| | - Sneha Gautam
- Department of Civil Engineering, Karunya Institute of Technology and Sciences, Tamil Nadu, Coimbatore, 641 114, India
- Water Institute, A Centre of Excellence, Karunya Institute of Technology and Sciences, Tamil Nadu, Coimbatore, 641 114, India
| | - Karan Singh
- Department of Physics, Hemvati Nandan Bahuguna Garhwal University, Srinagar, Uttarakhand, India
| | - Yogesh Kumar
- Department of Physics, Hansraj College, University of Delhi, North Campus, Malka Ganj, New Delhi, 110007, India
| | - Poonam Jain
- Department of Physics, Sri Aurobindo College, University of Delhi, Malviya Nagar, New Delhi, 110017, India
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Liang Y, Gui K, Che H, Li L, Zheng Y, Zhang X, Zhang X, Zhang P, Zhang X. Changes in aerosol loading before, during and after the COVID-19 pandemic outbreak in China: Effects of anthropogenic and natural aerosol. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159435. [PMID: 36244490 PMCID: PMC9558773 DOI: 10.1016/j.scitotenv.2022.159435] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/22/2022] [Accepted: 10/10/2022] [Indexed: 06/03/2023]
Abstract
Anthropogenic emissions reduced sharply in the short-term during the coronavirus disease pandemic (COVID-19). As COVID-19 is still ongoing, changes in atmospheric aerosol loading over China and the factors of their variations remain unclear. In this study, we used multi-source satellite observations and reanalysis datasets to synergistically analyze the spring (February-May) evolution of aerosol optical depth (AOD) for multiple aerosol types over Eastern China (EC) before, during and after the COVID-19 lockdown period. Regional meteorological effects and the radiative response were also quantitatively assessed. Compared to the same period before COVID-19 (i.e., in 2019), a total decrease of -14.6 % in tropospheric TROPOMI nitrogen dioxide (NO2) and a decrease of -6.8 % in MODIS AOD were observed over EC during the lockdown period (i.e., in 2020). After the lockdown period (i.e., in 2021), anthropogenic emissions returned to previous levels and there was a slight increase (+2.3 %) in AOD over EC. Moreover, changes in aerosol loading have spatial differences. AOD decreased significantly in the North China Plain (-14.0 %, NCP) and Yangtze River Delta (-9.4 %) regions, where anthropogenic aerosol dominated the aerosol loading. Impacted by strong wildfires in Southeast Asia during the lockdown period, carbonaceous AOD increased by +9.1 % in South China, which partially offset the emission reductions. Extreme dust storms swept through the northern region in the period after COVID-19, with an increase of +23.5 % in NCP and + 42.9 % in Northeast China (NEC) for dust AOD. However, unfavorable meteorological conditions overwhelmed the benefits of emission reductions, resulting in a +20.1 % increase in AOD in NEC during the lockdown period. Furthermore, the downward shortwave radiative flux showed a positive anomaly due to the reduced aerosol loading in the atmosphere during the lockdown period. This study highlights that we can benefit from short-term controls for the improvement of air pollution, but we also need to seriously considered the cross-regional transport of natural aerosol and meteorological drivers.
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Affiliation(s)
- Yuanxin Liang
- State Key Laboratory of Severe Weather, Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China; Department of Atmospheric and Oceanic Sciences, Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
| | - Ke Gui
- State Key Laboratory of Severe Weather, Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Huizheng Che
- State Key Laboratory of Severe Weather, Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China.
| | - Lei Li
- State Key Laboratory of Severe Weather, Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Yu Zheng
- State Key Laboratory of Severe Weather, Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Xutao Zhang
- State Key Laboratory of Severe Weather, Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Xindan Zhang
- State Key Laboratory of Severe Weather, Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China; Department of Atmospheric and Oceanic Sciences, Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
| | - Peng Zhang
- Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites (LRCVES), FengYun Meteorological Satellite Innovation Center (FY-MSIC), National Satellite Meteorological Center, Beijing 100081, China
| | - Xiaoye Zhang
- State Key Laboratory of Severe Weather, Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
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