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Air Pollution Scenario over Pakistan: Characterization and Ranking of Extremely Polluted Cities using Long-Term Concentrations of Aerosols and Trace GasesPakistan ranks third in the world in terms of mortality attributable to air pollution, with aerosol mass concentrations (PM2.5) consistently well above WHO (World Health Organization) air quality guidelines (AQG). However, regulation is dependent on a sparse network of air quality monitoring stations and insufficient ground data. This study utilizes long-term observations of aerosols and trace gases to characterize and rank the air pollution scenarios and pollution characteristics of 80 selected cities in Pakistan. Datasets used include (1) the Aqua and Terra (AquaTerra) MODIS (Moderate Resolution Imaging Spectroradiometer) Level 2 Collection 6.1 merged Dark Target and Deep Blue (DTB) aerosol optical depth (AOD) retrieval products; (2) the CAMS (Copernicus Atmosphere Monitoring Service) reanalysis PM1, PM2.5, and PM10 data; (3) the MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) reanalysis PM2.5 data, (4) the OMI (Ozone Monitoring Instrument) tropospheric vertical column density (TVCD) of nitrogen dioxide (NO2), and VCD of sulfur dioxide (SO2) in the Planetary Boundary Layer (PBL), (5) the VIIRS (Visible Infrared Imaging Radiometer Suite) Nighttime Lights data, (6) MODIS Collection 6 Version 2 global monthly fire location data (MCD14ML), (7) population density, (8) MODIS Level 3 Collection 6 land cover types, (9) AERONET (AErosol RObotic NETwork) Version 3 Level 2.0 data, and (10) ground-based PM2.5 concentrations from air quality monitoring stations. Potential Source Contribution Function (PSCF) analyses were performed by integrating with ground-based PM2.5 concentrations and the NOAA (National Oceanic and Atmospheric Administration) HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) air parcel back trajectories to identify potential pollution source areas which are responsible for extreme air pollution in Pakistan. Results show that the ranking of the top polluted cities depends on the type of pollutant considered and the metric used. For example, Jhang, Multan, and Vehari were characterized as the top three polluted cities in Pakistan when considering AquaTerra DTB AOD products; for PM1, PM2.5, and PM10 Lahore, Gujranwala, and Okara were the top three; for tropospheric NO2 VCD Lahore, Rawalpindi, and Islamabad and for PBL SO2 VCD Lahore, Mirpur, and Gujranwala. The results demonstrate that Pakistan’s entire population has been exposed to high PM2.5 concentrations for many years, with a mean annual value of 54.7 μg/cu. m, over all Pakistan from 2003 to 2020. This value exceeds Pakistan’s National Environmental Quality Standards (Pak-NEQS, i.e., <15 μg/cu. m annual mean) for ambient air defined by the Pakistan Environmental Protection Agency (Pak-EPA) as well as the WHO Interim Target-1 (i.e., mean annual PM2.5 <35 μg/cu. m). The spatial analyses of the concentrations of aerosols and trace gases in terms of population density, nighttime lights, land cover types, and fire location data, and the PSCF analysis indicate that Pakistan’s air quality is strongly affected by anthropogenic sources inside of Pakistan, with contributions from surrounding countries. Statistically significant positive (increasing) trends in PM1, PM2.5, PM10, tropospheric NO2 VCD, and SO2 VCD were observed in ~89%, ~67%, ~48%, 91%, and ~88% of the Pakistani cities (80 cities), respectively. This comprehensive analysis of aerosol and trace gas levels, their characteristics in spatio-temporal domains, and their trends over Pakistan, is the first of its kind. Results will be helpful to the Ministry of Climate Change (Government of Pakistan), Pak-EPA, SUPARCO (Pakistan Space and Upper Atmosphere Research Commission), policymakers, and the local research community to mitigate air pollution and its effects on human health.
Document ID
20210026201
Acquisition Source
Goddard Space Flight Center
Document Type
Accepted Manuscript (Version with final changes)
Authors
Muhammad Bilal
(Nanjing University of Information Science and Technology Nanjing, China)
Alaa Mhawish
(Nanjing University of Information Science and Technology Nanjing, China)
Janet E. Nichol
(Brighton and Sussex University Hospitals NHS Trust Brighton, United Kingdom)
Zhongfeng Qiu
(Nanjing University of Information Science and Technology Nanjing, China)
Majid Nazeer
(East China University of Technology, Nanchang China)
Md. Arfan Ali
(Nanjing University of Information Science and Technology Nanjing, China)
Gerritt de Leeuw
(Royal Netherlands Meteorological Institute De Bilt, Netherlands)
Robert C. Levy
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Yu Wang
(Nanjing University of Information Science and Technology Nanjing, China)
Yang Chen
(Wuhan University Wuhan, China)
Lunche Wang
(China University of Geosciences Wuhan, China)
Yuan Shi
(Chinese University of Hong Kong Shatin, Hong Kong, China)
Max P. Bleiweiss
(New Mexico State University Las Cruces, New Mexico, United States)
Usman Mazhar
(Zhejiang University Hangzhou, Zhejiang, China)
Luqman Atique
(Nanjing University of Information Science and Technology Nanjing, China)
Song Ke
(Jiangsu Provincial Institute of Geological Survey Nanjing, China)
Date Acquired
December 29, 2021
Publication Date
July 27, 2021
Publication Information
Publication: Remote Sensing of Environment
Publisher: Elsevier
Volume: 264
Issue Publication Date: October 1, 2021
ISSN: 0034-4257
URL: https://www.sciencedirect.com/science/article/pii/S0034425721003370
Subject Category
Geosciences (General)
Funding Number(s)
WBS: 953005.02.01.01.34
PROJECT: National Key Research and Development Program of China 2016YFC1400901
PROJECT: Special Project of Jiangsu Distinguished Professor R2018T22
CONTRACT_GRANT: National Natural Science Foundation of China 41976165
PROJECT: Jiangsu Technology Project of Nature Resources KJXM2019042
OTHER: NUIST 2017r107
Distribution Limits
Public
Copyright
Use by or on behalf of the US Gov. Permitted.
Technical Review
External Peer Committee
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