Air Pollution and Economic Activity

Air Pollution and Economic Activity#

Research Objective#

This study posits that Air Pollution data can be used to supplement other variables such as Nightlights to show changes in economic activity, especially in countries where frequent, subnational economic indicators are unavailable.

The research objective is to assess how effectively NOx, SOx, CH4, and PM2.5 data serve as proxies for transportation, hydrocarbon and non-hydrocarbon manufacturing, and industrial activity.

Background and Historical Context#

Nitrogen Dioxide (NO2) is a byproduct of combustion and its concentration in the atmosphere has been used as a measure of economic activity [EMRRC23]. The short atmospheric life of NO2 (less than a day) means that densities are closely correlated to emissions [SP19]. Global concentration of NO2 is available through satellite remote sensing datasets, making it a valuable resource for many data-poor countries.

We examined NOx as a potential proxy for transportation activity and industrial production/manufacturing. In Baghdad, Iraq, NOx levels correlated with the Traffic Congestion Intensity Index (TCI). In Ethiopia, NOx linked to industrial production via export values in Addis Ababa—the closest available proxy. Data gaps prevented combined transportation and manufacturing analysis in a single country context.

These findings revealed a positive NOx-TCI relationship in Baghdad and NOx-export value association in Addis Ababa. They also highlighted the need for testing in additional contexts and accounting for differential contribution from economic sectors.

To account for testing in different contexts, the team would like to expand this research using the case studies of Pakistan, Algeria and Myanmar. Both Algeria and Myanmar have GDP estimates from Transportation, Industry, Construction and other sources.

To account for sector decomposition of economic contributions to pollution, the team would like to add CH4 and PM2.5 to the existing models. This will allow the team to have PM2.5 estimates that are extremely useful in identifying changes in transportation. This will also allow teams to separate hydrocarbon sectors from the others.

Research questions#

How can we decipher PM2.5 levels, independent of NO2 and SO2, using existing remote sensing, and satellite imagery?

Which sectors of the economy against which GDP is typically measured, can Air Pollution be the best proxy?

Is there a relationship between GDP of the specific sectors identified and the environmental pollution variables?

Case Studies

  • Pakistan: The Pakistan Transportation Economists would like to understand how traffic or changes in transportation contribute to PM2.5. The team’s model for PM2.5 estimates, along with NO2 will be used by the Pakistan team in their transportation analysis.

  • Algeria: The students will have access to hydrocarbon and non-hydrocarbon GDP from Algeria. They will also have a list of sectors against which Algerian GDP is measured for RQ #2. This data can be used to understand the relationship between GDP sectors and Air pollution variables.

  • Myanmar: The students will be able to access subnational, sectoral, quarterly GDP estimates for Myanmar (still in review). The sectors here can also be used for RQ #2.

Existing Work

Imperial College London x World Bank Data Lab: Includes Literature Review, and final report

Project Team#

George Washington University

  • Tyler Wallet

  • Aakash Hariharan (Team Lead)

  • Maxi Leiding

  • Aishitha Pachipala

  • Relsy Puthal

  • Munir Salihu

  • Siddharth Saravanan

World Bank

  • Sahiti Sarva

  • Claudia Calderon Machicado

  • Maria Sol Tadeo

  • Farhan Reynaldo Hutabarat

References#

AKN22

Achyuta Adhvaryu, Namrata Kala, and Anant Nyshadham. Management and shocks to worker productivity. SSRN Working Paper, Social Science Research Network, 2022. URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4051825.

AXS22

Muhammad Ahmed, Zhen Xiao, and Yijun Shen. Estimation of ground pm2.5 concentrations in pakistan using convolutional neural network and multi-pollutant satellite images. Remote Sensing, 14(7):1735, 2022. doi:10.3390/rs14071735.

BMP21

missing journal in Bricongne2021

CGZGN16

Tom Chang, Joshua Graff Zivin, Tal Gross, and Matthew Neidell. The effect of pollution on worker productivity: evidence from call centers and factory environments. American Economic Journal: Economic Policy, 8(3):151–172, 2016. doi:10.1257/pol.20150085.

CGZGN19

Tom Chang, Joshua Graff Zivin, Tal Gross, and Matthew Neidell. The effect of pollution on worker productivity: evidence from call centers. American Economic Journal: Applied Economics, 11(1):151–172, 2019. doi:10.1257/app.20160436.

CSZ24

Q. Chen, K. Shao, and S. Zhang. Enhanced pm2.5 estimation across china: an aod-independent two-stage approach incorporating improved spatiotemporal heterogeneity representations. Journal of Environmental Management, 368:122107, 2024. doi:10.1016/j.jenvman.2024.122107.

dGVR+20

J. A. de Gouw, J. P. Veefkind, E. Roosenbrand, B. Dix, J. C. Lin, J. Landgraf, and P. F. Levelt. Daily satellite observations of methane from oil and gas production regions in the united states. Scientific Reports, 10(1):1379, 2020. URL: https://doi.org/10.1038/s41598-020-57678-4, doi:10.1038/s41598-020-57678-4.

DDG12

Sagnik Dey and Larry Di Girolamo. Variability of outdoor fine particulate (pm2.5) concentration in the indian subcontinent: a remote sensing approach. Remote Sensing of Environment, 127:153–161, 2012. doi:10.1016/j.rse.2012.08.021.

DWZ+16

Qian Di, Yun Wang, Antonella Zanobetti, Yaguang Wang, Petros Koutrakis, Christine Choirat, Francesca Dominici, and Joel D. Schwartz. Assessing pm2.5 exposures with high spatiotemporal resolution across the continental united states. Environmental Science & Technology, 50(9):4712–4721, 2016. doi:10.1021/acs.est.5b06121.

EMRRC23

Ishac Ezran, Stephen D. Morris, Martin Rama, and Daniel Riera-Crichton. Measuring global economic activity using air pollution. Policy Research Working Paper 10445, World Bank, 2023. URL: https://doi.org/10.1596/1813-9450-10445, doi:10.1596/1813-9450-10445.

Far25

missing journal in Farooqui2025

FMK+23

V. E. Fioletov, C. A. McLinden, N. A. Krotkov, and others. Version 2 of the global catalogue of large anthropogenic and volcanic so2 sources and emissions derived from satellite measurements. Earth System Science Data, 15:75–103, 2023. URL: https://essd.copernicus.org/articles/15/75/2023/.

GZN12

Joshua Graff Zivin and Matthew Neidell. The impact of pollution on worker productivity. American Economic Review, 102(7):3652–3673, 2012. doi:10.1257/aer.102.7.3652.

GC06

Pawan Gupta and Sundar A. Christopher. Satellite remote sensing of particulate matter and air quality assessment over global cities. Atmospheric Environment, 40(30):5880–5892, 2006. doi:10.1016/j.atmosenv.2006.03.016.

HO15

Rema Hanna and Paulina Oliva. The effect of pollution on labor supply: evidence from a natural experiment in mexico city. Journal of Public Economics, 122:68–79, 2015. doi:10.1016/j.jpubeco.2014.10.004.

HLS19

Guojun He, Tong Liu, and Alberto Salvo. Severe air pollution and labor productivity: evidence from industrial towns in china. American Economic Journal: Applied Economics, 11(1):173–201, 2019. doi:10.1257/app.20170286.

HWDW22

W. He, B. Wang, Danish, and Z. Wang. Impact of collaborative agglomeration of manufacturing and producer services on air quality: evidence from the yangtze river economic belt. Atmosphere, 13(6):966, 2022. URL: https://doi.org/10.3390/atmos13060966, doi:10.3390/atmos13060966.

PN25

S. Parubets and H. Naito. Predicting economic activity using atmospheric nitrogen dioxide (no₂) satellite data: evidence from local economic indicators in japan. PLOS One, 20(12):e0337901, 2025. URL: https://doi.org/10.1371/journal.pone.0337901, doi:10.1371/journal.pone.0337901.

RT22

Mohammad M. Rahman and George D. Thurston. A hybrid satellite and land use regression model of source-specific pm2.5 and pm2.5 constituents. Environment International, 163:107233, 2022. doi:10.1016/j.envint.2022.107233.

SP19

John H. Seinfeld and Spyros N. Pandis. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change. Wiley, 3rd edition, 2019.

SvDJ+26

S. Shen, A. van Donkelaar, N. Jacobs, C. Li, and R. V. Martin. Enhancing estimation of fine particulate matter chemical composition across north america by including geophysical a priori information in deep learning with uncertainty quantification. ACS ES&T Air, 2026. doi:10.1021/acsestair.5c00251.

vDMBB10

A. van Donkelaar, R. V. Martin, M. Brauer, and B. L. Boys. Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environmental Health Perspectives, 118(6):847–855, 2010. doi:10.1289/ehp.0901623.

vDMB+16

A. van Donkelaar, R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors. Environmental Science & Technology, 50(7):3762–3772, 2016. doi:10.1021/acs.est.5b05833.

vDMP06

A. van Donkelaar, R. V. Martin, and R. J. Park. Estimating ground-level pm2.5 using aerosol optical depth determined from satellite remote sensing. Journal of Geophysical Research: Atmospheres, 111(D21):D21201, 2006. doi:10.1029/2005JD006996.

WLC+21

P.-Y. Wong, H.-Y. Lee, Y.-C. Chen, Y.-T. Zeng, Y.-R. Chern, N.-T. Chen, S.-C.-C. Lung, H.-J. Su, and C.-D. Wu. Using a land use regression model with machine learning to estimate ground-level pm2.5. Environmental Pollution, 277:116846, 2021. doi:10.1016/j.envpol.2021.116846.

WorldBank21

World Bank. World Development Report 2021 : Data for Better Lives. World Bank, 2021. License: CC BY 3.0 IGO. URL: http://hdl.handle.net/10986/35218.

WorldBank24

World Bank. Pollution and labor productivity. Policy Research Working Paper, World Bank Group, 2024. URL: https://openknowledge.worldbank.org/.

License#

This project is licensed under the MIT License together with the World Bank IGO Rider. The Rider is purely procedural: it reserves all privileges and immunities enjoyed by the World Bank, without adding restrictions to the MIT permissions. Please review both files before using, distributing or contributing.

Contents