Developing Advanced PM2.5 Exposure Models in Lima, Peru 公开

Abstract

Background: There is convincing evidence of adverse health effects induced by exposure to PM2.5 in the growing body of literature. Lima’s topography and aging vehicular fleet results in severe air pollution with limited amounts of monitors to effectively quantify measurements for epidemiologic studies.

Objectives: We propose to develop a high-performance satellite-driving exposure model to estimate daily PM2.5 concentrations at a 1 km spatial resolution in Lima, Peru from 2010 to 2016 using a combination of ground measurements, aerosol optical depth (AOD), meteorological fields, parameters from atmospheric chemical transport models, and land use variables.

Methods: Parameters from the Weather Research and Forecasting model coupled with Chemistry (WRF-CHEM) and the European Centre for Medium-Range Weather Forecasts (ECMWF) were evaluated against ground monitoring stations from Weather Underground as well as ground PM2.5 measurements from the DIGESA and SENAMHI sites in Lima, Peru. A random forest model was used to gap-fill non-random missing satellite AOD data due to cloud cover to enhance spatial coverage and quality. Both a linear mixed effects model and a random forest model was used to fit AOD, WRF-CHEM, ECMWF, and land use parameters against ground measurements from 16 monitoring stations with available data between 2014 to 2016. Both models were then used to predict daily PM2.5 concentrations from 2010 to 2016. 

Results: The model fitting R² for the LME model was 0.63 and random forest model was 0.73. The overall cross-validation (CV) R² value and (RMSE) for the linear mixed effects model and random forest model was 0.58 (7.08 μg/m³) and 0.73 (5.66 μg/m³), respectively. The intercept and slope of the LME model was 0 and 1, compared to -2 and 1 from the random forest model, suggesting that the random forest underestimates PM2.5 compared to the LME model. Nonetheless, the random forest model performed better based on no change between model fitting R² and CV R².

Conclusions: Our prediction model allows for construction of long-term historical daily PM2.5 levels to support fundamental and imperative epidemiological studies that will likely impact governmental policies on air pollution in Lima, Peru.

Table of Contents

Table of Contents

1. Introduction ……………………………………………………………………………………………………………………………..                 ………. 1

1.1 PM2.5 and Health Impacts ……………………………………………………………………………………………….……….. 1

1.2 PM2.5 in Lima, Peru ………………………………………………………………………………………………………………….. 2

1.3 Limitations of Air Pollution Studies and Ground Measurements ……………………………………………….. 3

1.4 Remote Sensing Techniques …….……………………………………………………………………………………………….. 4

1.5 Study objectives ………………………………………………………………………………………………………………………… 6

2. Data and Methods …………………………………………………………………………………………………………………………….. 7

2.1 Study Area ………………………………………………………………………………………………………………………………… 7

2.2 Datasets and Processing ……………………………………………………………………………………………………………. 7

2.2.1 Ground Data ……………………………………………………………………………………………………………………. 7

2.2.2 Satellite Remote Sensing Data …………………………………………………………………………………………. 8

2.2.3 Chemical Transport Model Data ………………………………………………………………………………………. 9

2.2.4 Forecast Model ……………………………………………………………………………………………………………… 11

2.2.5 Miscellaneous Data ………………………………………………………………………………………………………… 11

2.3 Modeling Approach ………………………………………………………………………………………………………………… 16

2.3.1 LME Model …………………………………………………………………………………………………………………….. 16

2.3.2 Random Forest Model ……………………………………………………………………………………………………. 18

2.3.3 Cross Validation and Predictions ……………………………………………………………………………………. 18

3. Results …………………………………………………………………………………………………………………………………………….. 20

3.1 Descriptive Statistics ……………………………………………………………………………………………………………….. 20

3.2 Model Fitting and Validation …………………………………………………………………………………………………… 21

3.3 Prediction of PM2.5 Concentrations ……………………………………………………………………………………….. 23

4. Discussion and Conclusion ………………………………………………………………………………………………………………. 24

5. References ………………………………………………………………………………………………………………………………………. 26

6. Tables and Figures …………………………………………………………………………………………………………………………… 29

About this Master's Thesis

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School	Rollins School of Public Health
Department	Environmental Health
Degree	M.S.P.H.
Submission	Master's Thesis
Language	English
Research Field	Health Sciences, Public Health Remote Sensing Environmental Health
关键词	Lima air pollution atmospheric modeling
Committee Chair / Thesis Advisor	Liu, Yang, Emory University
Committee Members	Tolbert, Paige, Emory University
Partnering Agencies	Does not apply (no collaborating organization)

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