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6. Additional file 1 of Spatiotemporal characterization of aerosols and trace gases over the Yangtze River Delta region, China: impact of trans-boundary pollution and meteorology

Author

Javed, Zeeshan, Bilal, Muhammad, Qiu, Zhongfeng, Li, Guanlin, Sandhu, Osama, Mehmood, Khalid, Wang, Yu, Ali, Md. Arfan, Liu, Cheng, Wang, Yuhang, Xue, Ruibin, Du, Daolin, and Zheng, Xiaojun

Abstract

Additional file 1: Table S1. The spatial position of National monitoring stations over the studied cities of YRD region. Table S2. Criteria air pollutants in Hangzhou during the study period (2018-2021). Table S3. Criteria air pollutants in Nanjing during the study period (2018-2021). Table S4. Criteria air pollutants in Hefei during the study period (2018–2021). Table S5. Criteria air pollutants in Shanghai during the study period (2018–2021). Table S6. The standards for annual mean concentration (µg/m3) of different pollutants. Table S7. The relative (%) change in concentration of trace gases and particulate matter from 2019-2020. Table S8. Spring Festival during different Years in China. Table S9. Total variance and rotated component matrix of PCA of pollutants concentrations over Nanjing. Table S10. Total variance and rotated component matrix of PCA of pollutants concentrations over Hangzhou. Table S11. Total variance and rotated component matrix of PCA of pollutants concentrations over Shanghai. Table S12. Total variance and rotated component matrix of PCA of pollutants concentrations over Hefei. Figure S1. The inter- annual variation of NO2, O3, SO2, PM2.5 and PM10 in Hangzhou, Nanjing, Hefei and Shanghai during 2018-2021excluding COVID-19 lockdown days (24th of January–31st of March) from all years. Figure S2. PSCF Analysis based on NO2 grouped by season over Hefei. The color bar indicates the weights of Pollution source regions. Figure S3. PSCF Analysis based on SO2 grouped by season over Hefei. The color bar indicates the weights of Pollution source regions. Figure S4. PSCF Analysis based on O3 grouped by season over Hefei. The color bar indicates the weights of Pollution source regions. Figure S5. PSCF Analysis based on PM2.5 grouped by season over Hefei. The color bar indicates the weights of Pollution source regions. Figure S6. PSCF Analysis based on PM10 grouped by season over Hefei. The color bar indicates the weights of Pollution source regions. Figure S7. PSCF Analysis based on NO2 grouped by season over Hangzhou. The color bar indicates the weights of Pollution source regions. Figure S8. PSCF Analysis based on SO2 grouped by season over Hangzhou. The color bar indicates the weights of Pollution source regions. Figure S9. PSCF Analysis based on O3 grouped by season over Hangzhou. The color bar indicates the weights of Pollution source regions. Figure S10. PSCF Analysis based on PM2.5 grouped by season over Hangzhou. The color bar indicates the weights of Pollution source regions. Figure S11. PSCF Analysis based on PM10 grouped by season over Hangzhou. The color bar indicates the weights of Pollution source regions. Figure S12. PSCF Analysis based on NO2 grouped by season over Shanghai. The color bar indicates the weights of Pollution source regions. Figure S13. PSCF Analysis based on SO2 grouped by season over Shanghai. The color bar indicates the weights of Pollution source regions. Figure S14. PSCF Analysis based on O3 grouped by season over Shanghai. The color bar indicates the weights of Pollution source regions. Figure S15. PSCF Analysis based on PM2.5 grouped by season over Shanghai. The color bar indicates the weights of Pollution source regions. Figure S16. PSCF Analysis based on PM10 grouped by season over Shanghai. The color bar indicates the weights of Pollution source regions. Figure S17. Different regions of Peoples republic of China.

Published
2022
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8. Recommendations for HCHO and SO2 Retrieval Settings from MAX-DOAS Observations under Different Meteorological Conditions

Author

Javed, Zeeshan, primary, Tanvir, Aimon, additional, Bilal, Muhammad, additional, Su, Wenjing, additional, Xia, Congzi, additional, Rehman, Abdul, additional, Zhang, Yuanyuan, additional, Sandhu, Osama, additional, Xing, Chengzhi, additional, Ji, Xiangguang, additional, Xie, Mingjie, additional, Liu, Cheng, additional, and Wang, Yuhang, additional

Published
2021
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16. Recommendations for HCHO and SO 2 Retrieval Settings from MAX-DOAS Observations under Different Meteorological Conditions.

Author

Javed, Zeeshan, Tanvir, Aimon, Bilal, Muhammad, Su, Wenjing, Xia, Congzi, Rehman, Abdul, Zhang, Yuanyuan, Sandhu, Osama, Xing, Chengzhi, Ji, Xiangguang, Xie, Mingjie, Liu, Cheng, and Wang, Yuhang

Subjects
TRACE gases, ROOT-mean-squares, OPTICAL spectroscopy, LIGHT absorption
Abstract

Recently, the occurrence of fog and haze over China has increased. The retrieval of trace gases from the multi-axis differential optical absorption spectroscopy (MAX-DOAS) is challenging under these conditions. In this study, various reported retrieval settings for formaldehyde (HCHO) and sulfur dioxide (SO2) are compared to evaluate the performance of these settings under different meteorological conditions (clear day, haze, and fog). The dataset from 1st December 2019 to 31st March 2020 over Nanjing, China, is used in this study. The results indicated that for HCHO, the optimal settings were in the 324.5–359 nm wavelength window with a polynomial order of five. At these settings, the fitting and root mean squared (RMS) errors for column density were considerably improved for haze and fog conditions, and the differential slant column densities (DSCDs) showed more accurate values compared to the DSCDs between 336.5 and 359 nm. For SO2, the optimal settings for retrieval were found to be at 307–328 nm with a polynomial order of five. Here, root mean square (RMS) and fitting errors were significantly lower under all conditions. The observed HCHO and SO2 vertical column densities were significantly lower on fog days compared to clear days, reflecting a decreased chemical production of HCHO and aqueous phase oxidation of SO2 in fog droplets. [ABSTRACT FROM AUTHOR]

Published
2021
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17. Unraveling the Association Between Myocardial Infarction of Nonobstructive Coronary Arteries and Antiphospholipid Syndrome.

Author

Ramjas V, Jain A, Lee RDM, Fioni F, Tawfik N, Sandhu O, and Hamid P

Abstract

The term "myocardial infarction with nonobstructive coronary arteries (MINOCA)" refers to a condition characterized by clinical signs and symptoms consistent with acute myocardial infarction (AMI) (as defined by the third universal definition of infarction) and coronary arteries that are angiographically normal or nearly normal. A prominent source of morbidity and mortality in patients with antiphospholipid syndrome (APS) is thrombotic events. To evaluate whether there is a relation between APS and MINOCA in this research, we did an extensive assessment of the existing research in this field. According to the data, APS was associated with microvascular thrombosis, aberrant lipid metabolism, hypertension, and abnormalities of the coagulation cascade, among other conditions. Based on the available data, we discovered evidence that suggests a relationship between MINOCA and APS patients. It is vital to raise awareness of this concern among the general public. Also required is the development and implementation of diagnostic and targeted treatment guidelines for patients with APS and MINOCA., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2021, Ramjas et al.)

Published
2021
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