mkaufmann

EDIT: The old version had used the wrong conversion factors, now corrected

The central number is the exhaust mass of 110,000 pounds per hour. So how much is this really? This is about 50 metric tons of methane per hour. To be able to compare it with other greenhouse emissions we can calculate the CO2 equivalent by multiplying with 0.01133[1] giving the rate of: 1247 metric tons CO2e per hour.

Using the EPA Online tool[2] we can relate this to the toal emissions in Calfornia or the US. The total emission of methan measured in CO2e for California in 2014 was: 9,546,270 metric tons CO2e. Converted to a rate per hour this gives: 1089 metric tons CO2e per hour.

So while the well is leaking it is releasing 114% of the normal methane emissions of California.

Compared to all greenhouse gas emissions the well is causing an increase of 10% in california and 0.3%at the US national level compared to the emissions from large facilities.

The whole premise of the paper is that data can be analyzed in situ. That means loading from its original place in the original format without any previous transformations. This is in contrast to the traditional approach of database systems that the data has to be loaded first into a database.

This paper describes a way how unprocessed unindexed data can be efficiently used to answer queries using a database system. The novelty of this approach is mostly that they build a index on the fly that can be reused later and examining the idea of directly using the raw files. For efficient loading of CSV files which is also mentioned in the NoDB paper, I think those details are better described in a later paper from TU München[1] that examines this aspect in more detail.

HDT for RDF or Parquet[2] and ORCFiles[3] in the Big Data space, are (binary) formats where the data is already processed and stored in a more efficient format than plain old text CSV files. Creating these files can already be compared to just loading the data into a database. The only difference that format used for data storage is open and can be used by many systems. So its a completely different setting.

Still its an interesting thought to make databases aware of the indexed information in those file formats besides CSV so that these can also be directly used without loading.

When talking about pollutants is important to keeps the following points in mind:

- Is the pollutant effecting health?

- Is the pollutant effecting climate change?

- Is the amount of pollution locally concentrated or very distributed?

Cargo ships typically have a very high emission of nitrogen Oxides (NOx) and sulphur dioxide. When emitted by cars / factories on the mainland these often strongly contribute to harmful smog especially in megacities or cities with poor ventilation. Also they can be generally bad to the ecosystem also on the water due to causing acid rain etc.

The main pollutants on cargo ships have a very strong short term effect but are often out of the air in a few weeks. Because of that they don't rise to the atmosphere and don't directly contribute to long term climate changes. Thus I think the comparison in the article is very dangerous. When considering pollutants that effect climate change cars are more dominant.

So depending on which effects are discussed reducing pollutions from cars can still be benefitial. As an additional thought, the polutions of the cargo ships are spread out over a very large geographich area while the exhausts of cars are much more concentrated around cities. So when considering ones own quality of living, cars have a much bigger impact.