Including Bibliography

Including Bibliography#

Including a bibliography is of utmost importance in any academic or research work. A bibliography serves as a comprehensive list of all the sources consulted and referenced during the creation of a paper, essay, or any scholarly project. It not only adds credibility to the work but also demonstrates the depth of research conducted by the author. By providing a bibliography, the author acknowledges the contributions of other scholars and researchers, thereby showing respect for intellectual property and avoiding plagiarism.

Furthermore, a bibliography allows readers to delve deeper into the subject matter, explore related sources, and verify the accuracy and reliability of the information presented. In essence, the inclusion of a well-constructed bibliography is an essential aspect of scholarly writing that promotes transparency, authenticity, and the advancement of knowledge.

Usage#

See also

For a more complete list of in-line citation styles, check out the sphinxcontrib-bibtex.

To include the bibliography, we use the syntax as shown below. Note that this will include all citations throughout the book.

```{bibliography}
```

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(1,2): 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/.

To include only the local bibliography, we use the syntax as shown below.

```{bibliography}
:filter: docname in docnames
```

WorldBank21(1,2): 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.

Bibliography Styles#

`alpha`#

```{bibliography}
:style: alpha
```

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(1,2): 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/.

`plain`#

```{bibliography}
:style: plain
```

1: 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.
2: 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.
3: missing journal in Bricongne2021
4: 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.
5: 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.
6: 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.
7: 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.
8: 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.
9: 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.
10: 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.
11: missing journal in Farooqui2025
12: 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/.
13: 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.
14: 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.
15: 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.
16: 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.
17: 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.
18: 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.
19: 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.
20: John H. Seinfeld and Spyros N. Pandis. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change. Wiley, 3rd edition, 2019.
21: 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.
22: 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.
23: 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.
24: 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.
25: 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.
26(1,2): 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.
27: World Bank. Pollution and labor productivity. Policy Research Working Paper, World Bank Group, 2024. URL: https://openknowledge.worldbank.org/.

`unsrt`#

```{bibliography}
:style: unsrt
```

1: 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.
2: John H. Seinfeld and Spyros N. Pandis. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change. Wiley, 3rd edition, 2019.
3: 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.
4: 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.
5: 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.
6: 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.
7: 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.
8: 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.
9: 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.
10: 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.
11: 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.
12: 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.
13: 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.
14: 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.
15: 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/.
16: missing journal in Bricongne2021
17: 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.
18: 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.
19: missing journal in Farooqui2025
20: 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.
21: 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.
22: 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.
23: World Bank. Pollution and labor productivity. Policy Research Working Paper, World Bank Group, 2024. URL: https://openknowledge.worldbank.org/.
24: 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.
25: 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.
26: 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.
27(1,2): 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.

`unsrtalpha`#

```{bibliography}
:style: unsrtalpha
```

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.
SP19: John H. Seinfeld and Spyros N. Pandis. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change. Wiley, 3rd edition, 2019.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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/.
BMP21: missing journal in Bricongne2021
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.
Far25: missing journal in Farooqui2025
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.
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.
WorldBank24: World Bank. Pollution and labor productivity. Policy Research Working Paper, World Bank Group, 2024. URL: https://openknowledge.worldbank.org/.
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.
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.
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.
WorldBank21(1,2): 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.

Including Bibliography

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Including Bibliography#

Usage#

Bibliography Styles#

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