Gis for Environmental Justice – an Example Essay Example
Gis for Environmental Justice – an Example Essay Example

Gis for Environmental Justice – an Example Essay Example

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  • Pages: 12 (3092 words)
  • Published: December 17, 2017
  • Type: Research Paper
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Investigating the Scales of Environmental Justice Through Geographic Information Systems (GIS) And Spatial Analysis for Air Toxics in West Oakland, California (Fisher et al, 2006)

The analyzed paper examines the spatial point pattern of air toxins in West Oakland, California, which is an Environmental Justice area designated by the U.S. Environmental Protection Agency (EPA). Fisher et al. utilize a GIS framework and the interdisciplinary statistical technique known as Ripley's K-function to confirm that the neighborhood qualifies as an environmental justice site on various spatial scales. By using an air dispersion model, they also determine the potential number of individuals affected by a specific facility. Additionally, they address the issue of non-point sources of diesel emissions by analyzing the street network (Fisher, 2003; Fisher et al, 2006).

The data sources for this study include the US Census Bureau’s 2

...

000 survey, known as census 2000, and the Toxic Releases Inventory (TRI) which provides information on volume and location of emissions from facilities. The census is a statistical method of collecting data from every unit within a population or universe. Unlike administrative data sources, the information obtained from the census does not require transformation to be statistically useful. There are advantages and disadvantages to using the census method, according to Glejberman (1997).

The strengths of the census are: total coverage of the population with no sampling variance, the ability to cross-tabulate various demographic, social, and economic data, the option to present results per administrative units or smaller classification criteria, periodic updates that make it a reference for continuous statistics, and the opportunity to select samples and design further studies. Additionally, the census is the only procedure that allows investigation o

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low-frequency events and representation of detailed small sub-groups of the population. It also enjoys broad acceptance by the population.

However, there are limitations to using a census. These include high human and material costs involved in conducting it.

  • A wide organization is needed that covers and controls the whole universe, avoiding omission and duplication of data.
  • Delay in obtaining results.
  • Response burden: every member of the target population needs to provide data.
  • As a result, information obtained can be of lower precision/quality (due to a bigger error, both in data collection and processing) than if other sampling techniques had been used.

The Census 2000 has been acclaimed to be "the largest peacetime effort in the history of the United States", a motto made popular by the Bureau and widely spread through the media (see Edel 1999 as an example). The Bureau provides census tract boundary files (referred to small geographic areas with 2,500 to 8,000 inhabitants), containing demographic and socio-economic data for the entire nation. However, it is impossible to cover every person, and in the last decades, some cities have claimed that the mail questionnaire method results in an undercount of minorities. Different sampling techniques were proposed in order to adjust the final count (Pence, 1997).

The 2000 census did not accurately reflect the population due to political reasons. It was suggested that more localized versions of the census could have been used to obtain more accurate results. The Census Bureau's TIGER files and Tiger Digital Map Database were created to assist with the tabulation of census data using arc-node topology. Unlike TRI data, these files are available for every county in the country and are geocoded based on street addresses.

The

Toxic Releases Inventory (TRI) is the second primary source of data. It provides valuable information on pollutant facilities and, as stated in the official brochure (U.S. EPA, 2006), aids in identifying potential risks and measuring progress towards reduction goals. By inputting a Zip code into the TRI Explorer, interested parties can discover toxic chemical releases in their neighborhood. However, certain counties in California have no data available, suggesting incomplete information.

The sample picture below shows the counties in black that do not have any data, as mentioned earlier: Figure 1: Some counties do not present any TRI data. The accuracy and consistency of the information in the TRI database, which is reported by facility managers, are unknown. This limitation reduces its usefulness in determining actual emission quantities (Maantay, 2005). Additionally, the provided quantities are estimates, not measured amounts (Jia and Di Guardo, 1996). It is important to note that TRI data only indicate the disposal of chemicals and do not reflect the level of public exposure to them.

According to the U. S. EPA (2006), releases of highly toxic or persistent substances in low volumes may have more serious consequences compared to releases of less toxic chemicals in high volumes. Additionally, chemical conversion to a less toxic form can occur due to meteorology or microorganisms.

The article being reviewed does not consider certain phenomena such as thermal inversion. It is insufficient to only analyze these phenomena from a geometric perspective without taking into account the physical constraints, as mentioned by Gonzalez-Ferreiro (2006) and Chakraborty and Armstrong (1997).

The study should include wind maps as cartographies.

GIS for monitoring environmental injustice: the role of spatial analysis

Environmental justice (EJ) has

gained popularity in various areas, including socio-political, juridical, and scientific research. Examples of previous phenomena that are strongly linked to geographic location include the NIMBY syndrome (Not In My Backyard), which refers to local protests against undesirable facilities in their community, and related terms such as LULU (Locally Unwanted Land Use), BANANA (Build Absolutely Nothing at All Near Anybody), and PIBBY effect (Put it in Blacks' Backyards). These concepts ultimately contribute to the idea of environmental racism (Barbalace, 2001; Bosque-Sendra, 2006). GIS-based analysis is commonly used to assess environmental injustice, such as the disproportionate exposure of vulnerable communities to pollution (Maantay, 2005). The Agency references previous research studies in this field that utilize the provided data sources (U.S.).

The Asian Pacific Environmental Network (EPA, 2003) utilized TRI and demographic data to create maps displaying the presence of "toxic hot spots" in the San Francisco Bay Area where many impoverished Asian and Pacific Islanders reside. Public health researchers employed geo-referenced data from the TRI on toxic chemical releases and air dispersion modeling to estimate the potential health risks faced by a particular population and establish links between health outcomes and estimated health risk based on geographical points (Dent et al, 2000; U. S. EPA, 2003).

The estimated ethnic composition in West Oakland from Census 2000 may underestimate the size of the poor and immigrant population, as mentioned earlier. According to the study, the area has only a 9% population of white Caucasians, showing a racially diverse population and a high percentage of low-income residents. In order to examine the spatial realities of environmental injustice, various statistical techniques and methodological approaches involving GIS have been employed (Bosque-Sendra, 2001 and

2004; Bullen,1996; Chakraborty and Armstrong, 1997; Chakraborty et al., 1999; Maantay, 2005; Pollock and Vistas, 1995; Sheppard et al., 1999). The uses of GIS include epidemiological inquiries, disease mapping, environmental health and racism analyses, exposure modeling, and risk assessment (Bosque-Sendra, 2001; Maantay, 2005; Pine and Diaz, 2000). GIS provides solutions to spatial queries by allowing researchers to analyze outcome data to identify patterns and determine if there are statistically significant "spatial relationships between sources of pollution burdens and the characteristics of potentially affected populations" (Maantay, 2005). This is crucial when designating a neighborhood or municipality as an environmental justice priority site by the U.

S. EPA highlights the limitations of using GIS for environmental justice research, which include spatial and attribute data deficiencies, methodological problems related to geographical considerations, and difficulty in assessing cumulative or synergistic impacts. These limitations include delineating the optimal study area extent, determining the level of resolution and unit of spatial data aggregation, and estimating the areal extent of exposure. Additionally, many sources of environmental burdens are not inventoried, leading to a lack of available data for these uses (Maantay, 2005).

The purpose of this study is to address the methodological problem of scale by treating it as a variable rather than a pre-determined measure. The aim is to provide a valid empirical demonstration of congruent clustering of emissions and minority residential populations, as well as to identify statistically significant clusters of point source polluters and examine the surrounding demographics. The evaluation of environmental injustice involves five steps: identifying the population near the facility, analyzing the demographics using GIS, determining the facilities and total affected population(s) to measure the cumulative pollution burden, conducting

a disparate impact analysis by comparing to unaffected populations, and assessing the significance of the disparity through standard statistical methods.

  1. Identify the population in proximity to the facility;
  2. Examine the demographics of the affected population through GIS;
  3. Determine the facilities and total affected population(s), thus obtaining the cumulative pollution burden of neighboring facilities;
  4. Conduct a disparate impact analysis by comparing to unaffected populations;
  5. Determine the significance of the disparity through standard statistical methods.

The authors use an integration of air dispersion modeling and GIS via plume models to measure air pollution. They estimate the demographics affected by the most hazardous point-source facilities using the US EPA’s Industrial Source Complex Short Term (ISCST3) air dispersion model, which avoids circular buffers. This approach follows previous studies by Chakraborty and Armstrong that focus on characterizing environmental equity (Chakraborty and Armstrong, 1997).This approach can be employed to create a buffer zone for approximating pollution levels at ground level as it disperses downwind from the origin. According to Juliana Maantay, she examines the challenges of integrating GIS and air dispersion models: "Although this method produces more accurate outcomes than a basic circular or linear buffer, there are various issues associated with using air dispersion models. The primary obstacle, which is hard to address, is the insufficiently accessible data necessary as inputs for the model."

The second limitation is that these models rely on specific meteorological data inputs, including average hourly wind speed and direction, stack height and diameter of the facility, and accurate emissions data on specific substances and their average hourly quantities and rates (Maantay, 2005). Unfortunately, the TRI reporting process does not provide much of this data, but the article does not address this

issue, leaving the quality of the data uncertain. Another issue is how to integrate the results from the air dispersion software into standard GIS applications.

The current commonly used method of solving this issue is still by manually digitizing the plume concentration contours as a layer in GIS, based on the model outputs. However, this approach is laborious and prone to errors (Maantay, 2005). The Gaussian plume model is a basic air dispersion model primarily used for point source emitters like coal-burning plants (Moncrieff and Clement, 2007). Scale, intensity, and Ripley’s K spatial analysis focus on the spatial scale of census tracts for environmental justice analyses with GIS in the US. This assumes that pollutant sources and population are evenly distributed. This paper aims to avoid using census tracts and instead conducts neighborhood-scale analysis. In essence, Ripley’s K multi-distance spatial analysis aims to identify significant clusters (MacLennan, 1991). It detects clusters of features within defined distances, classifying irregularly located points as either clustered, uniform, or random occurrences.

The text discusses the detection of cluster size and pattern scale, but acknowledges that assumptions of homogeneity may not apply to populated areas. The authors of a paper mentioned in the text state that intensity distributions show cluster locations and Ripley's K reveals their statistical significance. By analyzing at different scales, Ripley's K identifies the strongest operating pattern and examines if certain communities are disproportionately affected. Clusters are represented as peaks on a graph. The analysis process involves identifying clusters, testing their statistical significance, and conducting GIS analysis on communities and pollutant sources near the clusters.The potential cancer-causing exposure to acetaldehyde emissions from the Red Star Yeast facility in the

West Oakland cluster was evaluated using the BAAQMD health risk screening based on the US EPA's ISCST3 air dispersion model. Additionally, mobile source pollution was assessed by examining the road network and travel routes within and around the neighborhood.

Results

The two largest clusters in the East Bay (Alameda County) were found to be statistically significant. Additionally, the East Bay TRI clusters were specifically examined. While the intensity distribution did not clearly distinguish individual clusters, it did reveal three peaks according to Ripley's K plot. GIS Analysis for West Oakland was conducted next. "After determining the presence, scale, and location of clusters, the next step in any framework is to examine the communities within the extent of those clusters." Block-level data from the Census Bureau 2000 survey were analyzed, revealing a racial distribution of 65% African Americans (Black), 9% White, 7% Hispanic, 9% Asians, and 10% categorized as "Other" (including racial mixes, American Indians, Native Americans, and Hawaiians).

The median annual household income in the area was approximately $20-25,000US, which is lower compared to the surrounding areas.
To assess the emissions of acetaldehyde by Red Star Yeast, the ISCST3 air dispersion model was used in conjunction with the block-level population layer. This allowed determining the potential number of people impacted by Red Star Yeast. The assessment estimated that around 5,628 people could be affected, although the actual number of individuals residing within high-concentration areas is less than 268.
The primary source of mobile emissions in West Oakland is heavy diesel truck traffic passing through the community en route to the Port.


Two recommendations were evaluated using GIS:

The first recommendation is to install traffic barriers on prohibited streets.

  • The second recommendation is to create a designated truck route that does not pass through the neighborhood. A different driving route has been identified to bypass the densely populated area. Overall, the key findings include the identification of the problem's cause - the three roads leading into the community - and a solution recommendation - an alternative route around the community.
  • Personal comment on the results: It would have been helpful to have a comparative graph to determine the extent of the population's affected. The results are somewhat unclear and only present a few specific cases.

    Here is a simplified graph from another study that I believe summarizes the results more conveniently (Gonzalez-Ferreiro, 2006):

    References

    1. Roberta C. Barbalace.
    2. Environmental Justice and the NIMBY Principle. environmental chemistry. om. 2001. Accessed on-line: 10/30/2008 http://EnvironmentalChemistry. com/yogi/hazmat/articles/nimby.
    3. Fisher et al. Scales of environmental justice: Combining GIS and spatial analysis for air toxics in West Oakland, California.

    The article "Health; Place 12 (2006) 701-714." by Amy Edel can be found at http://ora.ouls.ox.ac.uk/objects/uuid:824f4d17-7251-4636-a6c7-117e194be37e/datastreams/Attachment01. It was accessed on-line on 10/24/2008.

    Recruiting for Census 2000 Around the Island. Floral Park Dispatch 1999 http://www. antonnews. com/floralparkdispatch/1999/09/24/news/census. html Accessed on-line: 10/20/2008

    • Kenneth E.

    Foote and Margaret Lynch. Data Sources for GIS. 1995. Accessed on-line: 10/25/2008 http://www.colorado.edu/geography/gcraft/exam/data/data.html.

    • Pence, Richard 1997 U.

    S. Federal Census 2000, A Statistical Census? http://www.iigs.org/newsletter/9706news/uscen.

    htm. en. Accessed

    online: 10/21/2008 6. Glejberman, David 1997. Statistics. Accessed on-line: 10/21/2008 http://www.

    gestiopolis1. com/recursos7/Docs/ger/estadisticas-en-la-gestion-de- empresas.htm

    Forkenbrock, Jason Sheeley. Effective Methods for Environmental Justice Assessment. Transportation Research Board, 2004. Accessed on-line: 10/22/2008 http://books.google. com/books?

  • Rakesh Bahadur, William Samuels, and John Williams 1998.
  • ESRI User Conference '98. Application of Geographic Information Systems in Studies of Environmental Justice. Accessed on-line: 10/27/2008 http://gis.esri.com/library/userconf/proc98/PROCEED/TO150/PAP128/P128.HTM

    census.gov/main/www/cen2000.html.

  • How the Census Works
  • htm Accessed on-line: 10/20/2008
    U. S.Census Bureau, Introduction to Census 2000 Data Products http://www. census. gov/prod/2001pubs/mso-01icdp.

    The PDF was accessed online on 10/22/2008. The tiger census website was accessed online on 10/21/2008. The California Census website was accessed online on 10/20/2008. The website www. was also accessed online.

    esri.com/data/download/census2000_tigerline/index.html Accessed on-line: 10/20/2008

    John Moncrieff and Robert Clement. Environmental Pollution 3: Dispersion Modelling. 2007. Accessed on-line: 10/29/2008. http://www.geos.

    ed. ac. uk/abs/research/micromet/java/plume.html

  • Dent AL et al. Using GIS to study the health impact of air emissions. Drug Chem Toxicol. 2000 Feb;23(1):161-78.
  • The abstract was accessed online on 10/28/2008 at http://lib.bioinfo.pl/pmid:10711396.

    The book "Advances in remote sensing and GIS analysis" was written by Peter M. Atkinson and Nicholas J. Tate in c1999.

    Briggs D. discusses the role of GIS in coping with space (and time) in air pollution exposure assessment.

    The abstract for the article "J Toxicol Environ Health A" from July 9-23, 2005, with the volume and issue number 68(13-14) and page numbers 1243-61, can be accessed online at http://www.ncbi.nlm.nih.gov on October 23, 2008.

    gov/pubmed/16024500?rdinalpos=1=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_Discovery_RA=2=relatedarticles=pubmed

  • 20. Paul Brindley et al. The effect of alternative representations of population location on the areal interpolation of
  • air pollution exposure Computers, Environment and Urban Systems. Volume 29, Issue 4, 2005, Pages 455-469. Accessed on-line: 10/30/2008 http://www.

    sciencedirect.com/science?

  • Chinniah, G. Mapping human exposure to air pollution using geographical information systems (GIS) case studies: city of Southampton. Dissertation, Kingston University, 2003. Pp. 12-22
  • Joshua Benjamin Fisher, Environmental Justice / Air Toxics analysis for West Oakland: Combining GIS and spatial data analysis. 131st Annual Meeting of the American Public Health Association, 2003 http://apha.
  • Accessed on-line: 10/24/2008
    Margaret Gordon. Environmental Justice Tour. West Oakland Environmental Indicators Project.

    org, at http://ej4all.org/organizations.php?area=West=68 Accessed on-line: 10/28/2008

  • http://www.epa.gov/tri/tridata/tri04/brochure/brochure.htm Accessed on-line: 10/20/2008
  • gov/tri/triprogram/FactorsToConPDF.pdf Accessed on-line: 10/20/2008

    li>sasmacr. tristart. macro Accessed on-line: 10/20/2008

    li>EPA, 2003. How Are the Toxics Release Inventory Data Used? – government, business, academic, and citizen uses.

    http://www.epa. gov/tri/guide_docs/pdf/2003/2003_datausepaper.df Accessed on-line: 10/23/2008

    Colleen T.Moynihan. An environmental justice assessment of the light rail expansion In Denton county, Texas. Thesis for the Degree of Master of Science.

    2007. Accessed on-line: 10/30/2008 http://digital.library.unt.edu/permalink/meta-dc-3934:1

    • Gonzalez-Ferreiro.Cartografia de exposicion a riesgos tecnologicos mediante modelos de dispersion atmosferica y mapa de vientos. 2006 Accessed on-line: 10/20/2008
    • Irwin Weintraub.

    Fighting Enviromental Racism: A Selected Annotated Bibliography.

    1. Electronic Green Journal, Issue 1, June 1994 http://www.mapcruzin.com/ejigc.tml Accessed on-line: 10/22/2008 Maggi Kelly. Advanced GIS for the University of California Cooperative Extension: Spatial Analysis. 2007. http://gif.berkeley.edu/CE/Summer2007/AdvGIS_PM-Lecture_UCCE.ppt Accessed on-line: 10/28/2008
    2. Juliana Maantay. Asthma and air pollution in the Bronx: Methodological and data considerations in using GIS for environmental justice and health research.

    2005. Accessed on-line: 10/25/2008 http://lib.bioinfo.pl/pmid:10711396

  • 34. Bosque Sendra, J.

  • et al. A new model for identifying undesirable

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