DATA DRIVEN FRAMEWORK FOR ANALYSIS OF AIR QUALITY LANDSCAPE FOR THE CITY OF LAHORE

Abstract. Several Pakistani cities are among the world’s most polluted. In the previous three years, air pollution in Lahore has been considerably over World Health Organization guideline levels, endangering the lives of the city’s more than 11 million citizens. In this paper, we investigate the city’s capability to combat air pollution by analyzing three essential aspects: (1) Data, (2) Capacity, and (3) Public awareness. Several studies have reported the need for expansion of the current air quality monitoring network. In this work, we also provide a context-aware location recommendation algorithm for installing the new air quality stations in Lahore. Data from four publicly available reference-grade continuous air quality monitoring stations and nine low-cost air quality measuring equipment are also analyzed. Our findings show that in order to measure and mitigate the effects of air pollution in Lahore, there is an urgent need for capacity improvement (installation of reference-grade and low-cost air quality sensors) and public availability of reliable air quality data. We further assessed public awareness by conducting a survey. The questionnaire results showed huge gaps in public awareness about the harms of the air quality conditions. Lastly, we provided a few recommendations for designing data-driven policies for dealing with the current apocalyptic air quality situation in Lahore.

Location
Deutsche Nationalbibliothek Frankfurt am Main
Extent
Online-Ressource
Language
Englisch

Bibliographic citation
DATA DRIVEN FRAMEWORK FOR ANALYSIS OF AIR QUALITY LANDSCAPE FOR THE CITY OF LAHORE ; volume:XLVIII-4/W5-2022 ; year:2022 ; pages:167-173 ; extent:7
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XLVIII-4/W5-2022 (2022), 167-173 (gesamt 7)

Classification
Soziale Probleme, Sozialdienste, Versicherungen

Creator
Rahman, A.
Usama, M.
Tahir, M.
Uppal, M.

DOI
10.5194/isprs-archives-XLVIII-4-W5-2022-167-2022
URN
urn:nbn:de:101:1-2022102005243428656358
Rights
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
15.08.2025, 7:35 AM CEST

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Associated

  • Rahman, A.
  • Usama, M.
  • Tahir, M.
  • Uppal, M.

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