Shrestha, J.; Khan, R.K.; McClintock, S.; DeGroote, J.; Zeman, C.L. Correlations between Educational Struggle, Toxic Sites by School District and Demographic Variables, with Geographical Information System Projections. Int. J. Environ. Res. Public Health2023, 20, 7160.
Shrestha, J.; Khan, R.K.; McClintock, S.; DeGroote, J.; Zeman, C.L. Correlations between Educational Struggle, Toxic Sites by School District and Demographic Variables, with Geographical Information System Projections. Int. J. Environ. Res. Public Health 2023, 20, 7160.
Shrestha, J.; Khan, R.K.; McClintock, S.; DeGroote, J.; Zeman, C.L. Correlations between Educational Struggle, Toxic Sites by School District and Demographic Variables, with Geographical Information System Projections. Int. J. Environ. Res. Public Health2023, 20, 7160.
Shrestha, J.; Khan, R.K.; McClintock, S.; DeGroote, J.; Zeman, C.L. Correlations between Educational Struggle, Toxic Sites by School District and Demographic Variables, with Geographical Information System Projections. Int. J. Environ. Res. Public Health 2023, 20, 7160.
Abstract
contaminated sites located in each school district. Previous studies showed that various environmental factors, such as exposure to toxic substances negatively impacts human health depending on their concentration and occurrence. Therefore, the study aims to use Geographical Information System (GIS) technology and secondary data on IEP numbers of each school district, chemical occurrence in contaminated sites, and demographic data to conduct a correlational analysis. The secondary data obtained from school districts and contaminated sites from Iowa Department of Natural Resources Facility Explorer were populated in ArcMap 10.5 (a GIS software) for generating maps and data to conduct statistical analysis. The contaminants were categorized into metals, polyaromatic hydrocarbons (PAH), and solvents with weighing factors as 1, 0.5, and 0.25. A total of 1 Superfund site and 39 CERCLA sites were identified as contaminated sites for this study. The majority of contaminants were heavy metals such as lead, arsenic, mercury, and cadmium. The mean toxic score of all contaminated sites combined was 13.4 (sd 14.4).
The correlational analysis between the IEP numbers from each school districts and toxic scores from the contaminated sites was positive indicting that increased toxic score increased the number of students enrolled in the IEP program (F=23.7, p<0.0001). The correlational analysis between toxic score and demographics indicated some interesting results. Children under the age of 10 were living in areas with high toxic score and the showed mild correlation (p<0.00052). Similarly, higher number of black population resided in areas with high toxic score (p=0.0032) than the white population (p<0.0001). The result also indicated that children enrolled in IEP were predominantly among the black population (p<0.0001). The correlational analysis between household income and poverty percentage of people residing near the contaminated area indicated that people had low average household income (p=0.0002) and high poverty percentage (p=0.0203) residing in areas with higher toxic score. Regarding the educational status, less number of people with post graduate degree (p<0.0001) resided in areas with high toxic score and more number of people with no degree (p<0.0001) resided in areas with high toxic score. Finally, the study showed the increasing trend of eligible children enrolling in free and reduced lunch programs with increasing toxic scores (p=0.0012) and IEP levels (p=0.0416). This study emphasize on developing multiple exposure to correlate environmental factors contributing to the negative health outcome on people.
Public Health and Healthcare, Public, Environmental and Occupational Health
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