Comparison of carbon footprints of steel versus concrete pipelines for water transmission

<p>The global demand for water transmission and service pipelines is expected to more than double between 2012 and 2022. This study compared the carbon footprint of the two most common materials used for large-diameter water transmission pipelines, steel pipe (SP) and prestressed concrete cylinder pipe (PCCP). A planned water transmission pipeline in Texas was used as a case study. Four life-cycle phases for each material were considered: material production and pipeline fabrication, pipe transportation to the job site, pipe installation in the trench, and operation of the pipeline. In each phase, the energy consumed and the CO₂-equivalent emissions were quantified. It was found that pipe manufacturing consumed a large amount of energy, and thus contributed more than 90% of life cycle carbon emissions for both kinds of pipe. Steel pipe had 64% larger CO₂-eq emissions from manufacturing compared to PCCP. For the transportation phase, PCCP consumed more fuel due to its heavy weight, and therefore had larger CO₂-eq emissions. Fuel consumption by construction equipment for installation of pipe was found to be similar for steel pipe and PCCP. Overall, steel had a 32% larger footprint due to greater energy used during manufacturing.</p> <p><i>Implications</i>: This study compared the carbon footprint of two large-diameter water transmission pipeline materials, steel and prestressed concrete cylinder, considering four life-cycle phases for each. The study provides information that project managers can incorporate into their decision-making process concerning pipeline materials. It also provides information concerning the most important phases of the pipeline life cycle to target for emission reductions.</p

Interpreting the properties of skewness and kurtosis in histograms.

<p>Interpreting the properties of skewness and kurtosis in histograms.</p

Correlation coefficients between the features used in the multiple regression analysis.

<p>Correlation coefficients between the features used in the multiple regression analysis.</p

Comparison of image histogram in different tissues in responders and non-responders.

<p>Average histograms of GM image (left), WM image (middle), and CSF image (right) for non-responders (top row) and responders (bottom row).</p

Response versus features extracted from the WM composite image.

<p>Response versus features extracted from the WM composite image.</p

Summary of the imaging characteristics of the patients along with age and gender information.

<p>All patients had edema.</p

An FOV from multi-parametric MRI and the resulting composite images of a non-responder.

<p>(Patient ID: 178), 1st row: MR images before the treatment (T2-weighted, FLAIR, T1-weighted and T1-post, respectively from left to right). 2nd row: Composite images (WM, GM, CSF and Orthogonal feature, respectively from left to right). 3rd row: MR images, acquired 50 days after the treatment. Red ROIs show borders of Gd-enhanced region on different images.</p

Response versus features extracted from the CSF composite image.

<p>Response versus features extracted from the CSF composite image.</p

Response versus features extracted from the GM composite image.

<p>Response versus features extracted from the GM composite image.</p

Multiple regression results for the prediction of the response to therapy using imaging features.

<p>Multiple regression results for the prediction of the response to therapy using imaging features.</p