Obtaining regular, good-quality measurements of aerosol (fine particulate) pollutant concentrations is cost-prohibitive in much of the world. Over the last several years, many in the atmospheric sciences community have turned to satellite measurements - particularly measurements of aerosol optical depth (AOD) - as a proxy for these more detailed and cost-intensive ground measurements. Several dozen articles in peer-reviewed journals have reported strong agreement between satellite-based AOD and ground-based air quality measurements. However, this is not always the case.
Hersey et al. (2013) demonstrated that across much of South Africa, satellite-based AOD and ground-based aerosol concentrations are anti-correlated - a result of atmospheric dynamics and the inherent assumptions in satellite data retrievals. One of the reasons that this disagreement exists in South Africa is that winter pollution is typically confined to a very shallow layer (50-100 m) above the ground - a layer that satellites are unable to distinguish from the ground itself.
In this project, you will compare freely-available AOD data from NASA’s MODIS and MISR satellite platforms with AOD data from NASA’s ground-based AERONET stations around the Johannesburg in order to determine how important these shallow layers of aerosol pollutants are. It is hypothesized that these shallow layers of pollutants will result in a large discrepancy between ground and satellite data during winter.
Deliverables from this project include figures comparing multi-year averages for the annual cycle in AOD from the satellite and ground platforms, as well as creative ways for displaying discrepancies between the two. A statistical comparison of satellites with ground-based data will be necessary as well. Students must obtain some basic working knowledge about the AOD measurement and how it is made, as well as the general dynamics of aerosol pollution in South Africa (you can learn this by reading the aforementioned paper). Results should be written up in a brief report that takes the structure of a scientific journal article, leaning heavily on figures and statistical results.
Particularly interested students will have the opportunity to continue this research project over the summer and turn this paper into a journal article to be submitted for publication.
Poor air quality from aerosol pollution is the second leading cause of premature death in the world, with the burden of this pollution falling primarily on the poor. When governments seek to improve air quality, the easiest target tends to be large point-sources of pollutants (i.e. power generation facilities, refineries, and other industrial facilities). However, this strategy is ineffective in areas such as South Africa, where the vast majority (up to 80%) of particulate matter can be apportioned to domestic burning of coal and wood in low-income areas for the purpose of heating and cooking.
To this end, a major chemical company in South Africa funded a multi-year pollution study in a small township called Kwadella during 2013-2014. This study consisted of one year (winter and summer) of air quality measurements to establish a baseline for pollution. After year 1, low-cost modifications were made to township homes to improve their thermal efficiency with the goal of reducing the quantity of fuel burned. Year 2 made the same air quality measurements to determine the impact of these intervention steps on both indoor and ambient air quality.
A dataset exists with a number of different parameters related to air quality and thermal comfort of homes:
Indoor and outdoor temperature time series
Indoor and outdoor particulate matter concentrations at several size resolutions
Indoor and outdoor gas-phase pollutant concentrations
There are many questions that could be answered with this rich dataset, including:
Is there a way to automate detection of the beginning of fire burning from thermal data indoors? What about the addition of fuel to stoves?
Is there a statistically significant relationship between indoor+outdoor particulate concentrations and the amount of fuel burned?
Did the interventions work (i.e. did they achieve a statistically significant reduction in particulates)?
Deliverables will vary depending on the question answered by the student team. It may be well-documented code with demonstration of its effectiveness, or it may be a short report in the format of a peer-reviewed journal article that relies heavily on figures and statistical analysis.
Data are easily obtained from Scott Hersey, who is also available to guide your research questions. Particularly interested students will have the opportunity to continue this research project over the summer with the goal of producing an article to submit for publication in a peer-reviewed journal.