High Resolution Air Pollution Mapping for Louisiana

Alberto Lopez Rueda, Alexandra Hurst,

Amanda Murray, Kory Kirshenbaum

Problem Statement

Air Pollution is a Major Concern in Louisiana

“You gotta live here to try to breathe the air, drink the water, see the children so sick and watch people die.”

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-Geraldine Watkins

Concerned Citizens of St. John the Baptist Parish

Source: ProPublica 2019^[1] (adapted)

Source: CNN 2017 ^[2]

Lack of Sensors (and Trust)

• Only 9 NO₂ sensors in Louisiana vs. more than 50 in eastern Texas
• Louisiana Department of Environmental Quality (LDEQ) has conflicting priorities

Source: LA Illuminator 2023 ^[3]

“We have enough problems without running industry out”

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-State Senator Bodi White

In response to a proposed bill that would require fence line air monitors

Our Project

Data?

Project Goal:

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Support local research and advocacy efforts to understand and reduce impacts of air pollution in Louisiana.

Research Question:

Creating The Dataset

Advancing Research

Supporting Advocacy

Pollution Estimate Dashboard

Our Contribution:

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• Only up-to-date, high-resolution dataset of NO₂ for the state of Louisiana

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• Use Sentinel-5P TROPOMI, the highest resolution satellite remote sensing tool currently in orbit to improve model accuracy

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• Innovative machine learning techniques

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• Accurate, open-source, accessible

Sensor Placement Algorithm

First regional analysis of disparities in individual pollutant exposure

High-resolution weekly ground level NO2

estimates for Louisiana for 2019 - 2023

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Creating

The Dataset

Supporting

Advocacy

Advancing Research

Creating

The Dataset

The Gap: No up-to-date high-resolution NO₂ data

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Our Objective: Use machine learning to develop an open-source NO₂ dataset at a resolution of 1km² using the state-of-the-art Sentinel-5P satellite

Data Sources

X values

Sentinel-5P Satellite Data

17 Additional Covariates

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• Meteorological variables (wind, temperature, etc.)

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• Population density

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• Road density

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• Elevation

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• Impervious surface

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• Mobility

Y values

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NO2 Ground-Sensors

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~50 sensors across

Louisiana and Texas

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Tropospheric NO₂ Concentrations

(3.5 x 5.5 km resolution)

Data Pipeline

Satellite Data

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X

Y

Additional Covariates

Sensor Values

Modeling Overview

ML & Deep Learning Techniques

FFNN

1D CNN

XGBoost

TabNet�tabular transformer

Final Ensemble�XGBoost + FFNN + TabNet

Minimized RMSE using 10-fold

Sensor Cross-validation

Full Coverage Ground-Level �Predictions

Data Pipeline

Satellite Data

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X

Y

Additional Covariates

Sensor Values

Accurate and Reliable Predictions

* RMSE divided by mean observed NO2 values

Better results than most similar published research

Highly accurate predictions in new, unseen locations

Prediction

Actual

Sample sensor from test set

Predictions and Residuals

Residuals by Sensor

Results Comparable to Highly-Cited Research

*Difference is metrics, methods, and calculations makes it challenging to compare directly across research papers

Center For Air, Climate, and Energy Solutions National NO2 Dataset^[4]

1979-2015, National

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Cross Validation R²=0.87

(Annual, Block Groups)

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Cited in health and disparity studies

Anenberg et al. (2022)^[5]

1990-2019, Global

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Cross Validation R²=0.54

(Annual, 1km x 1km)

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Cited in pediatric asthma and disparity studies

Atmospheric Composition Analysis Group (St. Louis University)^[6]

2005-2019, Global

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Test R²=0.53

(Annual, 2.8 km x 2.8 km)

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Authors >100 publications since 2019

Comparison to Expert-Recommended NO2 Datasets

Summary

Developed the only up-to-date, high resolution dataset for NO₂ in Louisiana. Comparable to existing state-of-the art models.

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Advancing

Research

Our Objective: Demonstrate how our high resolution air pollution dataset can enhance existing environmental justice research in the region.

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Prior Research

Terrell et al. 2022^[7]

Disparities in NO₂ emissions by Census Tract

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“We didn’t have access to a good dataset for NO2 concentrations”- K. Terrell

Our Addition

Disparities in NO₂ pollution by Census Block

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~170 km²

(average)

<1 km²

(average)

Community <> Community

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Individual <> Individual

Block

Tract

Disparity Analysis Methodology

Assign Pollution by Census Block Centroid*

Aggregate and Average by Race^**

5 ppb

*Methodology adapted from: Sun-Young Kim, Matthew Bechle, Steve Hankey, Lianne Sheppard, Adam A. Szpiro, and Julian D. Marshall. 2020. Concentrations of criteria pollutants in the contiguous U.S., 1979 – 2015: Role of prediction model parsimony in integrated empirical geographic regression. PLOS ONE, 15(2):e0228535.

**Methodology adapted from: arah E. Chambliss, Carlos P. R. Pinon, Kyle P. Messier, Brian LaFranchi, Crystal Romeo Upperman, Melissa M. Lunden, Allen L. Robinson, Julian D. Marshall, and Joshua S. Apte. 2021. Local- and regional-scale racial and ethnic disparities in air pollution determined by long-term mobile monitoring. Proceedings of the National Academy of Sciences, 118(37).

* NH signifies non-hispanic or latinx

**Other includes american indian or alaskan native, native hawaiian or pacific islander, other, or two or more races

Pollution exposure is assigned based on the average amount of pollution at the centroid of the census block. “Black” and “White” are specified in the graph because these demographics make up over 80% of the population (56% and 31% respectively).

Summary

Identify demographic disparities in individual pollution exposure

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Lay the groundwork for future research identifying drivers of disparate exposure

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“Majority black communities experience higher emissions on average”

“Black and brown individuals are exposed to higher pollution on average”

Supporting

Advocacy

Our Objective: Demonstrate two applications of our dataset for local environmental advocacy groups to support existing efforts to monitor and reduce air pollution.

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Application 1: Pollutant Estimation Dashboard - WIP

The Problem

Our Solution

Advocacy groups lack good data on air pollution to support their efforts to combat air pollution.

Rely on modeled emissions data

High resolution pollutant dashboard that can supplement advocacy efforts to reduce health and safety risks.

Application 2: Sensor Placement Algorithm

The Problem

Our Solution

Interpretable, unbiased, and customizable algorithm to determine placement of new ground-level NO₂ monitors.

Lack of trust in the placement process of ground level sensors.

Lack of updated data to substantiate locations for new sensors

Align metrics to EPA considerations for existing sensors:

• Mean NO₂
• Max NO₂
• Population
• Susceptible and Vulnerable Populations

Summary

Develop two high-impact applications of our dataset that can be used to support advocacy

Advocate Needs

Our Solutions

Quickly identify regions with higher pollutant levels

Determine where to place sensors for greatest effect

Next Steps

Include additional sensors from Clarity Movement Co.

Include additional Criteria Air Pollutants (SO₂, O₃)

Present at Louisiana Environmental Conference and Trade Fair (hopefully!)

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Creating

The Dataset

Supporting

Advocacy

Data

Change

Advancing Research

"This team's machine learning approach is an important part of identifying the sources of air pollution in Louisiana, and the communities that are impacted."

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- Alex Kolker, Louisiana Universities Marine Consortium

Questions?

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Questions?

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To learn more, visit:

bit.ly/louisianaairpollution

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Acknowledgements

D. Alex Hughes and Uri Schonfeld

University of California, Berkeley

All the wonderful people who spent their time meeting with us and responding to our many emails to support us with our project!

Alex Kolker

Associate Professor at Louisiana Universities Marine Consortium

References

• Younes, L., Shaw, A., Perlman, C. 2019. In a Notoriously Polluted Area of the Country, Massive New Chemical Plants Are Still Moving In. [online] ProPublica. Available at: https://projects.propublica.org/louisiana-toxic-air/.
• Blackwell, V., Drash, W. and Lett, C. (2017). Toxic tensions in the heart of ‘Cancer Alley’. [online] CNN. Available at: https://www.cnn.com/2017/10/20/health/louisiana-toxic-town/index.html.
• Sullivan, C. (2023). Lawmakers reject plea from Cancer Alley residents for fence line air monitoring. [online] Louisiana Illuminator. Available at: https://lailluminator.com/2023/06/14/cancer-alley/.
• Sun-Young Kim, Matthew Bechle, Steve Hankey, Lianne Sheppard, Adam A. Szpiro, and Julian D. Marshall. 2020. Concentrations of criteria pollutants in the contiguous U.S., 1979 – 2015: Role of prediction model parsimony in integrated empirical geographic regression. PLOS ONE, 15(2):e0228535.
• Susan C. Anenberg, Arash Mohegh, Daniel L. Goldberg, Gaige H Kerr, and Michael Brauer. 2022. Long-term trends in urban NO2 concentrations and associated paediatric asthma incidence: estimates from global datasets. The Lancet Planetary Health, 6(1).
• Mark E Cooper, R M Martin, Chris A McLinden, and Jeffrey R Brook. 2020. Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument. Environmental Research Letters, 15(10):104013–104013.
• Kimberly A. Terrell, Gianna St. Julien, Discriminatory outcomes of industrial air permitting in Louisiana, United States, Environmental Challenges, Volume 10, 2023, 100672, ISSN 2667-0100.

Original slide template and icons: https://slidesgo.com/theme/air-pollution-infographics#search-Pollution&position-28&results-87

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Clipart for data pipeline from https://publicdomainvectors.org/

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Logo was made with the help of ChatGPT-4