S-Trap Example

S-Trap drains are often prohibited because unlike P-Trap drains, S-Trap drains easily lose their water seal.

Plastic S-Trap in Package

The parts of the S-Trap include a J-Bend.  The J-bend is also used in the P-Trap.  The S-Trap is also made up of a curved sink trap, which attaches the J-bend to the floor pipe.

The J-Bend (labeled C), Raised Sink Trap, and elongated Floor Pipe.

Center to End and Thread Engagement measurements on the J Bend and Sink Trap.  Fitting allowance calculations on the J Bend and Sink Trap.  End to End, Inside Diameter and Outside Diameter measurements on the floor drain.

Inside, outside, and end to end measurements on the washers.  Thread engagement measurement on the threaded connector.

Parts and measurements.

Cementing PVC Pipe and Fittings

PVC pipe can be primed with a primer/cleaner and cemented with a solvent cement.  After measuring fitting allowance, cutting and chamfering pipes, and cleaning pipes and fittings, parts are primed to temporarily soften the plastics and then quickly cemented together.

I primed this pipe 1/2" past the thread of the fitting.  Then I primed and cemented the tee fitting and PVC Pipe.

Reading Water Quality Data from MWRD (II)

This time I tried to summarize open data available for each month in 2014, not just January.

Data Source:  Metropolitan Water Reclamation District of Chicago http://www.mwrd.org/    > Reports   >   M&R Data and Reports   >   Water Quality Data   >   2014 Waterway Ambient Water Quality   >  Waterways_2014.xlsx

Data Transformation:  I moved data on each month in 2014 into a single table (see SQL).

Data Analysis:  I uploaded the csv table into R for summary statistics (see R)

Mean Temperature Measurement for Each Month in 2014

> January_TEMP_AVE = mean(January_TEMP, na.rm=TRUE)

> February_TEMP_AVE = mean(February_TEMP, na.rm=TRUE)

> March_TEMP_AVE = mean(March_TEMP, na.rm=TRUE)

> April_TEMP_AVE = mean(April_TEMP, na.rm=TRUE)

> May_TEMP_AVE = mean(May_TEMP, na.rm=TRUE)

> June_TEMP_AVE = mean(June_TEMP, na.rm=TRUE)

> July_TEMP_AVE = mean(July_TEMP, na.rm=TRUE)

> August_TEMP_AVE = mean(August_TEMP, na.rm=TRUE)

> September_TEMP_AVE = mean(September_TEMP, na.rm=TRUE)

> October_TEMP_AVE = mean(October_TEMP, na.rm=TRUE)

> November_TEMP_AVE = mean(November_TEMP, na.rm=TRUE)

> December_TEMP_AVE = mean(December_TEMP, na.rm=TRUE)

> TEMP_Averages_2014 <- data.frame(January_TEMP_AVE, February_TEMP_AVE, March_TEMP_AVE, April_TEMP_AVE, May_TEMP_AVE, June_TEMP_AVE, July_TEMP_AVE, August_TEMP_AVE, September_TEMP_AVE, October_TEMP_AVE, November_TEMP_AVE, December_TEMP_AVE)

> print(TEMP_Averages_2014)

January_TEMP_AVE February_TEMP_AVE March_TEMP_AVE April_TEMP_AVE May_TEMP_AVE

1         4.391667          5.018182       5.252174       11.85313         16.6

  June_TEMP_AVE July_TEMP_AVE August_TEMP_AVE September_TEMP_AVE October_TEMP_AVE

1      22.48387      23.64375        24.40667           20.36429         15.01379

  November_TEMP_AVE December_TEMP_AVE

1          9.222222          6.038462

Data Summary:  I started summarizing the results by entering them into Java arrays (see Java).

Arrays for Average of Temperature Measurements in 2014

// Two, single column arrays

float pH2014 [] = new float [12];

pH2014[0] = (float) 6.8375;

pH2014[1] = (float) 7.15;

pH2014[2] = (float) 6.83913;

pH2014[3] = (float) 7.600937;

pH2014[4] = (float) 7.626563;

pH2014[5] = (float) 7.39871;

pH2014[6] = (float) 7.509688;

pH2014[7] = (float) 7.450667;

pH2014[8] = (float) 7.451786;

pH2014[9] = (float) 7.448621;

pH2014[10] = (float) 7.602593;

pH2014[11] = (float) 7.561923;

String[] months2014 = new String [12];

months2014[0] = "January";

months2014[1] = "February";

months2014[2] = "March";

months2014[3] = "April";

months2014[4] = "May";

months2014[5] = "June";

months2014[6] = "July";

months2014[7] = "August";

months2014[8] = "September";

months2014[9] = "October";

months2014[10] = "November";

months2014[11] = "December";

//Printing each data row

System.out.println("2014 Month | Average Measured pH");

System.out.println(months2014[0] + "    |    " + pH2014[0]);

System.out.println(months2014[1] + "    |    " + pH2014[1]);

System.out.println(months2014[2] + "    |    " + pH2014[2]);

System.out.println(months2014[3] + "    |    " + pH2014[3]);

System.out.println(months2014[4] + "    |    " + pH2014[4]);

System.out.println(months2014[5] + "    |    " + pH2014[5]);

System.out.println(months2014[6] + "    |    " + pH2014[6]);

System.out.println(months2014[7] + "    |    " + pH2014[7]);

System.out.println(months2014[8] + "    |    " + pH2014[8]);

System.out.println(months2014[9] + "    |    " + pH2014[9]);

System.out.println(months2014[10] + "    |    " + pH2014[10]);

System.out.println(months2014[11] + "    |    " + pH2014[11]);

System.out.println(months2014[12] + "    |    " + pH2014[12]);

Reading Water Quality Data from MWRD

Source: Metropolitan Water Reclamation District of Chicago http://www.mwrd.org/    > Reports   >   M&R Data and Reports   >   Water Quality Data   >   2014 Waterway Ambient Water Quality   >  Waterways_2014.xlsx

Data Transformation: I created a data table for analysis.  See SQL code.

Data Analysis: Printed summary statistics and K means cluster analysis results for January, 2014 in R.  See R code.

Summary:

• The average temperature of the waterways in January, 2014 was 4.79 degrees celsius.
• The average pH was 6.92.
• The average Total Dissolved Solids was 1141.86 mg/L.

Of these three measures of water quality, only Total Dissolved Solids appears to be normally distributed across the survey points.

I ran a K means cluster analysis with three clusters on the three variables (in R).

This is a summary table for k means cluster analysis with three clusters for TDS:

Location	Measured Value	Cluster	Cluster Average
North Branch Chicago River at Albany Ave	1552	Cluster 1	1560.0
Chicago Sanitary & Ship Canal at Cicero Ave	1132	Cluster 2	1159.5
Chicago Sanitary & Ship Canal at Harlem Ave	1276	Cluster 2	1159.5
North Branch Chicago River at Diversey Ave	1196	Cluster 2	1159.5
North Shore Channel at Touhy Ave	1054	Cluster 2	1159.5
South Fork South Branch Chicago River at Archer Ave	1080	Cluster 2	1159.5
Chicago River Main Stem at Wells St	668	Cluster 3	793.0
Higgins Creek at Wille Rd	808	Cluster 3	793.0
Little Calumet River at Halsted St	840	Cluster 3	793.0
South Branch Chicago River at Loomis St	856	Cluster 3	793.0

P-Trap Example

P-Traps are used to prevent back-flow from waste pipes back into fixtures and cross connections.

P-Traps work by using water pressure to prevent gasses from traveling back from waste pipes or sewers into the fixture.

The top of the water stored in the p-trap to prevent back-flow (the top of the water seal) is called the crown weir.


This P-Trap example is a 11/2" plastic p-trap from Home Depot:

Parts of the P-Trap in the bag.

Parts of the P-Trap included in the bag.

Inside and Outside Diameters of Connective Pieces.

End to End measurement, Outside and Inside Diameter of Wall Tube connecting P-Trap to waste pipe.

P-Trap and Waste Tube with Connective Pieces.

Improved Water Source Data

According to The World Bank, an 'improved water source' is a drinking water source from "piped water on premises (piped household water connection located inside the user’s dwelling, plot or yard), and other improved drinking water sources (public taps or standpipes, tube wells or boreholes, protected dug wells, protected springs, and rainwater collection)" (Worldbank.org).

Access to an improved water source is an important metric to follow, partly because it is one of the indicators used in the Millennium Development Goals.  'Ensure Environmental Sustainability' is one of the eight Millennium Development Goals and decreasing the number of people without an improved water source is one of the targets within that goal (un.org).

This metric is also a baseline metric for other water quality measurements.  The 'improved' classification for water sources is one step in what the World Health Organization and UNICEF have called the "water quality monitoring ladder, or pyramid" (WHO / UNICEF, pg. 12).  Above and beyond the improved or unimproved classification are water quality classes based on testing for chloroforms, testing for e coli, testing for chemicals and establishing a robust national water quality reporting service.

Data on the percentage of households with an 'improved water source' is available through the World Bank open data portal.  Their publicly available data on improved water source distinguished between urban and rural households.  Analyzing this data, I found that across 2005 and 2012, the percentage of urban households with an improved water source was 12.97% higher than the percentage of rural households with an improved water source (link).



References
Improved Water Source, World Bank.  Accessed from http://data.worldbank.org/indicator/SH.H2O.SAFE.ZS.

JMP Monitoring Drinking Water Quality Task Force Meeting Report, WHO / UNICEF (2010).  Accessed from: http://www.wssinfo.org/documents/?tx_displaycontroller[type]=reference_documents

Water Supply and Sanitation: Sector Results Profile, World Bank.  Accessed from: http://www.worldbank.org/en/results/2013/04/12/water-sanitation-results-profile (2015)

Goal 7: Ensure Environmental Sustainability, United Nations.  Accessed from: http://www.un.org/millenniumgoals/environ.shtml (2015)

Chamfering Pipes

In plumbing, ‘chamfer’ refers to what is usually a 45 degree angle at the end of a pipe.  Chamfering prevents the edges of a pipe from cutting its fittings.  Chamfering can be done with a chamfering tool; with PVC pipe, chamfering can also be done by hand with a file.

Chamfering the end of a PVC pipe to 45 degrees with a rasp file.