Final Project

Introduction:

As major nations like China, the US, and India continue to experience increased economic development and urbanization, our increased consumption of earth's natural resources and increased production of consumer-base waste is becoming more and more a critical issue. The essence of this issue lies in the population dynamics of our exponential growth, as shown by this chart by the Population Reference Bureau in Washington:

Interestingly, this shows a peak and suggests an eventual downfall. Humans have been changing, using, and disposing of earth’s natural resources for centuries. 

This map, based on data from the Laboratory for Anthropogenic Landscape Ecology, shows changes in ‘Anthromes’ or anthropogenic biomes modeled from 1700-2000. The vast orange areas illustrate the increased development of the natural world:

Through our development we have not only altered our natural environment but we have disrupted, destroyed, and disposed of much of it.  This map shows the progression of our development from the 1700s on the right to 2000 on the left and the overall change again above:

 

As a world leader with rapid urbanization and economic development, China’s growing population is creating serious urban waste issues. Municipal Solid Waste (MSW) grows by more than 10% each year and is totals more than 1000 pounds per person each year. This graph shows the rapid development of China over the last three centuries leading to it present condition of vast villages and settlements and numerous large urban centers:

Before the 1990s, formal collection and treatment of municipal solid waste in China was at a rate of less than 5%. Since then China has been making significant efforts in improving its standards and performance. Now over 80% of waste generated in China is landfilled, but less than 10% conform to US standards, and almost 50% are open dumps.

Collected waste is often mixed, making recycling and treatment efficiency difficult. Also, large amounts of waste generated outside of the urban centers often fails to reach the proper landfills and ultimately ends in uncontrolled local dumps or elsewhere on the way.

While countries like the US, Australia, and South Korea capture and store over half of their waste in controlled landfills, over half of the waste in other countries like China, Turkey, and other developing countries ends up in uncontrolled landfills or is dumped illegally. This map shows some of the top annual producers of waste including the US, Mexico, Australia, Brazil, Argentina, China, and others:

However, waste collection, transfer, and treatment standards and efficiency has been rapidly improving in recent times. Statistics show that municipal solid waste treatment plants increased their total average capacities from 193,000 tons/year to 257,000 tons/year within just the first 5 years of the past decade.

Gases emitted from landfills can have a serious impact on local environments as well as global climate change. Methane accounts for about 60% of this landfill gas and CO2 about 40%. Although CO2 remains in the atmosphere much longer than methane, methane contributes much more to the greenhouse effect.

Improving landfill technologies and standards is especially important for reducing greenhouse emissions and improving air quality, but also important for reducing the mounting problems of waste scattering, toxic wastewater seepage and runoff, groundwater contamination, odors, and many other environmental hazards. Our populations are not going to get any smaller and neither are our mounting trash problems.

As I became aware of these underlying issues in consumption and waste that have been developing over the centuries and will continue to plague us in the future led, I started to look into solutions, to at least prolong the inevitable. Coupled with China’s recent development and consumption has been efforts in reducing their impact and increasing their sustainability. I had done research on the technologies they are developing and am convinced that there is promising hope, but needed to determine more about by whom and where the effort should be made. I decided to focus on Xi'an in the Shaanxi Province of Chine:

Methods:

To do this, I gathered extensive data and performed spatial analysis on my findings. I used raster reclassifications and map algebra for the first few anthrome maps. I reclassified all of the anthrome cells based on the following impacts or levels of development:

Urban:                                     5

Mixed Settlements:                         4

Villages:                                     3

Crop Land:                                     2

Rangelands:                                     1

Barren and Woodlands:             0

With this newly reclassified data, I used the raster calculator with the command of ‘reclass2000’ – ‘reclass1700’. I took the resulting values of -4 to 5 and reclassified them based on the following: 

Became Less Developed:                         (-4) – (-1)

Remained the Same:                                     (-.99) – 0

Became More Developed:                        0 – 3

Became Much More Developed:             3.01 – 5

To make the MSW map I used data from a LFGTE company called Veolia Environmental Services and created a simple xcel spreadsheet, joined it with a world countries layer from the UCLA GIS data share, and created the appropriate symbology. 

Next I used data from the WWF biomes to create a map displaying the local biomes around Xi’an in the Shaanxi area. I created some additions to the map with the drawing tool, included a symbol based on the location of Xi’an, and performed basic clip functions.

Similar methods were used to create the map on the Xi’an focus area but data from the UCLA GIS data share was used.

I created the map locating existing power generating landfills in China by performing select by attribute queries using data from an online Global Administrative Areas forum and matching cities with the cities in the names of found LFGTE sites. 

For the suitability maps I used the same GAA data as well as a DEM and population grid from ESRI. I first converted the DEM to a grid of slope. Then reclassified the slope and population grids. I classified the flat areas and most populated areas as the highest. This allowed me to add them using single output math algebra. I reclassified the results again and then converted it to a shapefile. I clipped the final combined shapefile with the Xi’an city limit polygon.

I used the same data and layers for the Shaanxi region site suitability but selected by attributes for values > 3 which were areas of high population and low slope.

Results:

Through my methods and research I came to realize that there is great potential within China to develop and implement successful treatment technologies. I focused on the feasibility of the Xi’an area within the Shaanxi province.

Xi’an is a leader in innovation and on the forefront of China’s economic and urban development. It has also made significant efforts to do so sustainably and environmentally consciously. Based on carbon emissions and urban development, Xi’an was as the ranked by the National Bureau of Economic Research as the 44^{th ‘}most green’ city out of 74 major Chinese cites in 2006.

The Sanitation Bureau in Xi’an collects about 1,500 tons/day of domestic, commercial, industrial, and construction waste. About 80% of this waste is taken to the Jian Cun Gao landfill. In addition to Xi’an’s Sanmincun Waste Transfer Station, 100 small-scale waste transfer stations have been built around the city to improve collection measures. In 2009, coverage of waste collection increased to 99% in some areas of the city.

These measures were part of Xi’an’s efforts to obtain the status of a “National Hygienic City”, which it did in 2008. The rest of China’s is facing similar issues with waste accumulation and national policies and efforts are also aimed at increasing the capture amounts and efficiency.  

However, while these large scale and rapidly growing urban centers do need to increase their amount of waste capture and treatment, it is important what is done with this increasing volume of waste. There are many different types of treatment processes and procedures, but those that reuse the incoming waste are the most efficient.

The following collected data summarizes some of the existing landfill gas-to-energy (LFGTE) plants and technology in China:

            

Guangzhou Xingfeng LFGTE Plant:

Energy Capacity: 9 MW 

Energy Production: 200 MWh/day

Future Capacity: 13 MW

Treats 5000 cubic meters/hour of landfill gas

Largest landfill gas-to-energy plant in China.

Hangzhou Tianziling LFGTE Plant:

Energy Production: 15,700 KWh/year

Gas treated to date: 116.5 million cubic meters

Xi’an Jiang Cun Gao LFGTE Plant:

Energy Capacity: 7,500 KW

Energy Production: 40,000 KWh/year            

CO2 Reductions: 108,000 tons/year           

The only operating landfill in Xi’an city and has the second largest energy capacity in

China.

                                       Shanghai Laogang LFGTE Plant:                                   

Energy Capacity: 15 MW

Energy Production: 22,700 MWh/year

Gas Treated To Date: 19.42 million cubic meters

Guangzhou Datianshan LFGTE Plant:           

Energy Capacity: 1 1,060 KWh unit

Energy Production: 6,900 MWh/year

                                                                       

Foshan Gaoming LFGTE Plant:            

Capacity: 1,936 tons/day

Area: 23,980,000 cubic meters            

Average Depth: 100 meters

                       Future Energy Capacity: 6.5 MW                       

          Horizontal and vertical gas extraction pipes optimize gas-to-energy efficiency allowing its 8 generators to efficiently                        supply the local electricity grid.                                    

Hong Kong S.E.N.T. LFGTE Plant:                        

Energy Production: 12,203 MWh                        

Capacity: 49,000,000 cubic meters

Internal Thermal Energy Production: 38,430 MWh           

Close to the urban center of Hong Kong, it receives 39% of the Hong Kong’s waste.

             Beijing Asuwei LFGTE Plant:            

            

Energy Capacity: 2,700 KW

Energy Production: 20 Mil KWh/year

CO2 Reductions: 100,000 tons/year

Energy Supply: 17,000 homes/year

Methane Reductions: 13 million cubic meters/year

Suzhou LFGTE Plant:

Energy Capacity: 1.25 MW/unit with 4 units

Directly into the Suzhou City local power grid.

Each of these and more are marked in green on this map. 

Accessing the energy in the landfill gas is the best way to do this. As waste decomposes large amounts of heat is given off. Energy from this heat can be stored and used to produce power. Fuel can also be generated by processing certain components of municipal waste that are highly petroleum based. This not only accesses the energy stored in the waste but also diverts it from ending up in the ground or on the street for years and years to come. The following table from the Coral Reef Alliance puts some perspective on what ends up in landfills:

Waste	Time To Decompose
Notebook paper	3 months
Comic book	6 months
Wool mitten	1 year
Cardboard milk carton	5 years
Wooden baseball bat	20 years
Leather baseball glove	40 years
Steel can	100 years
Aluminum soda can	350 years
Plastic sandwich bag	400 years
Plastic six-pack ring	450 years
Polystyrene foam cup	Maybe never
Car tire	Maybe never
Glass bottle	Maybe never

 

Conclusion:

Estimates account landfills for 12% of global methane emissions, coal for 6%, and oil and gas for another 18%. In developing countries, landfills can contribute up to 40% of their methane emissions. Using LFGTE technologies will reduce each of these three sources of emissions. However, as previously mentioned, although these methane emissions have the potential to influence climate change, landfills pose a plethora of other serious environmental hazards as well. As inputs to landfills increase, solutions to these issues need to also. Converting landfill gas to energy, recycling and gasifying rubbers and plastics and other petroleum based waste, and properly incinerating waste are essential techniques. Based on its population quality and quantity, as well as topography, Xi’an is a prime place to increase the momentum of such practices.

 

This map displays that based on population density and slope, within Shaanxi the Xi'an city limits is near many suitable sites. 

References:

Xi’an power demand: http://www.cbw.com/business/invest/xian/index.htm

U.S. EPA, Global Anthropogenic Emissions of Non-CO2

Greenhouse Gases: 1990-2020 (EPA Report 430-R-06-003)

 ‘A Winning Combination of Renewable Clean Power with Greenhouse Gas (GHG) Reduction’. Hong Sima, Jan C. Hutwelker, Samuel A. Dean, David R. Horvath
Landfill Gas to Energy (LFGTE) Project

http://www.dri.edu/images/stories/editors/leapfrog/techprog/Vg_2_Sima.pdf

‘Putting People in the Map: Anthropogenic

Biomes of the World’. E Errllee  CC Ellis and Naavviinn  Ramankuty

Front Ecol Environ 2008; 6(8): 439–447.

‘Our Presence & Future’. Joe A. Zorn. Veolia Environmental Services In China.

http://siteresources.worldbank.org/INTUSWM/Resources/463617-1180624544541/J-ZORN.pdf

Landfill Gas to Energy Conversion Project, China South Pole Carbon Asset Management Ltd http://www.southpolecarbon.com/_marketing/259LFG_China.pdf

‘How Biodegradable is Your Trash?’ Coral Reef Alliance http://www.coral.org/node/3916

‘Summary Evaluation’ conducted by: Foundation for Advanced Studies on International Development (FASID) Report date: June 2009

http://www.jica.go.jp/english/operations/evaluation/grant_aid/pdf/solidwaste_china.pdf

‘Brief Introduction to Shaanxi Province and its Environment Protection’
Shaanxi environment protection 2004
http://www.snepb.gov.cn/en/menu.html

The Greenness of China: Household Carbon Dioxide Emissions and Urban Development

Siqi Zheng, Rui Wang, Edward L. Glaeser, and Matthew E. Kahn

NBER Working Paper No. 15621

December 2009

JEL No. Q5

China ShapeFiles extracted from GADM version 1.0, March 2009
Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License
http://www.gadm.org

Olson, D.M., E. Dinerstein, E.D. Wikramanayake, N.D. Burgess, G.V.N. Powell, E.C. Underwood, J.A. D’Amico, H.E. Strand, J.C. Morrison, C.J. Loucks, T.F. Allnutt, J.F. Lamoreux, T.H. Ricketts, I. Itoua, W.W. Wettengel, Y. Kura, P. Hedao, and K. Kassem. 2001. Terrestrial ecoregions of the world: A new map of life on Earth. BioScience 51(11):933-938.

                                                                                   

                                                                       

                                                                                                                       

Lab 8

This week’s lab was an exercise in interpolation. Using data from the LADWP, I used various interpolation methods to compare and assess precipitation levels for the county’s current season and the seasonal average. The most complex and time-consuming part of this lab was definitely compiling the data from the website into a spreadsheet. The following spatial analysis was straightforward and rather simple.

The first step was compiling the name, longitude, latitude, precipitation to date, and average precipitation of each station into a spreadsheet. This was then inserted as a layer in ArcGIS and the spatial analysis was made from this data. With the Los Angeles County as a spatial analysis mask, this data was added to multiple data frames. I interpolated the average and season to date precipitation data using the two different types of splines and two different powers of the inverse distance weighted method.

             This final out put includes the splines of both and the IDW of both but also the difference in the precipitation to date and the average precipitation using both a spline and IDW. Although each is relatively easy to do and as easy as the other, it is difficult to determine which method is most effective and accurate for this type of data. The differences in both methods are noticeable but it’s hard to determine which is more accurate. By doing more spatial analysis and more interpolations, I hope to gain more experience and learn more about these different methods. 

Lab 7

 

For this lab I created a fire hazard map for the area in the region of the 2009 Station Fire in Los Angeles County. This map was created using a raster digital elevation model layer, a raster layer with fuel sources based on vegetation coverage, and a vector layer with a polygon outlining the greatest extent of the Station Fire.

First, I converted the DEM into a raster layer showing the slope of elevation. I then reclassified this into five different categories from very low slope to very high slope. Then, using the reclassification parameters described in the tutorial, I reclassified the fuel source layer again into five different categories from very low flammability to very high.

I now had one slope and one vegetation layer, each with five different risk categories. Using the raster calculator, I added these two layers. Again I reclassified the result into five categories ranging from very low risk to very high risk. Finally I put the vector polygon of the greatest extent of the Station Fire over this resulting layer. The final map now shows the fire risk in the area of the Station Fire based on slope and coverage type. I have also included a map of risk based just on slope and another just on coverage type. This lab was relatively straightforward and was completed with minimal difficulties. I believe I am becoming much more proficient at using ArcGIS. 

I spoke too soon - for some reason the layers in the first map I uploaded did not upload. Here is the correct and final map now.

Lab 6

First Arithmetic Model:

[Reclass of Coverclass] + [Reclass of sl_dist] + [Reclass of Slope of elevation] + [Reclass of Soildrain] + [Reclass of Stream Buffers]

First Weighted Model:

(([Reclass of Soildrain] * .3) + ([Reclass of Slope of elevation] * .3) + ([Reclass of Stream Buffers] * .2) + ([Reclass of Coverclass] * .1) + ([Reclass of sl_dist] * .1)) * 5

Data: Vegetation type, slope of elevation, soil drainage data, and stream buffers.
Purpose: Used as a landfill development suitability analysis for the area in concern. 
Cell Size: 1000, 1000
Measurement Units: Meters
Geographic Extent: Gallatin County, Montana
Date Run: 02/13/11
Model Run For: Yongwei Sheng/Kettleman City Officials

This lab was a raster data spatial analysis exercise. I learned how to create, convert, and do spatial analysis on raster cells. Such skills have enhanced my ability to use GIS as a valuable tool for problem solving and decision-making.

I began this exercise in spatial analysis by defining the vector layer outlining the fictional county as an ‘analysis mask’. This ensured that the suitability analysis would be calculated based only on data from the county in concern.

Next I derived a layer representing the slope of elevation from a DEM of the county. Such a layer is useful for determining an area’s suitability for excavation.

Another issue to consider may be proximity to other areas of concern. In regard to landfill development, it is important to consider a new site’s proximity to nearby streams and other already existing landfills. A buffer is the tool for solving this issue. Here I created a buffer layer with four one-kilometer rings surrounding the rivers in the county.

This vector layer was then converted to a raster grid for spatial analysis. The resulting grid was a floating point grid representing the continuous elevation data. For ease of analysis, I converted this from a floating grid to an integer grid.

To determine the location of nearby existing landfills, I selected from the attribute table all of the area’s landfills that are currently open. I then made a new raster layer with grid values representing meters from any given point to the nearest open landfill.

After adding two additional raster grids describing the area’s soil and vegetation type, my data consisted of 5 layers representing 5 concerns to consider in a landfill development suitability analysis:
1) slope of elevation, 2) proximity to streams, 3) proximity to already operating landfills, 4) soil drainage, and 5) vegetation type.

The raster data in each cell of these five grids was then reclassified to create five new layers, each with cell values based on a scale of only 1-5. Each of these grid layers was assigned a particular weight based on their relative importance to this suitability analysis.

Then, using the raster calculator, the weighted values of each cell of each layer were combined. The resulting layer was reclassified to produce a final suitability layer. Each cell of this final grid considers each of the five weighted criteria and has a value of 1-5, 1 being the least suitable and 5 being the most suitable site for a landfill.

I have used these techniques to show which parts of a fictional area of Montana are the most suitable for a new landfill. But these spatial analysis techniques could also be used to solve numerous other suitability issues in land use pertaining to private and commercial real estate development, business expansion and location, resource allocation, city planning, etc.

These same techniques could be used to help solve the debate over the expansion of a landfill in California’s Central Valley. In addition to determining which areas are most suitable for expanding the landfill, these tools could also help determine whether or not the existing landfill is the cause of several contaminations and heath issues.

A buffer around vector layers locating aquifers, wells, streams, springs and other sources of water would tell if areas of the landfill were already close enough to these sources to contaminate them. A slope of elevation raster grid would help determine how contaminates are expected to spread from the landfill. And a raster grid representing the area’s soil drainage data would help determine if contaminants are draining down to groundwater below.

By using these techniques, investigators would know how close the landfill is to sources of water, how the contaminants flow from the landfill, and whether or not contaminants are draining down into groundwater. This would help enable them to determine whether or not the existing landfill is responsible for the current heath issues.