In order to have the scores spread out widely and range between 0 and 10, we normalized the mean values by dividing its maximum value and multiplying the entire data set by 10 to generate the CEHI value for each block group. The formula calculating CEHI is shown below in two steps:. CEHI inorm is the normalized cumulative environmental hazard index score for block group i. The SVI was developed to describe the sensitivity of the community to health challenges and resources to mitigate negative health impacts from environmental hazards.
The SVI is a relative measure with values between 0 and 1: the higher the value, the more vulnerable the residents of a block group are to the effects to environmental and other hazards.
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In this study we consider the presence of in-patient health care facilities within given block groups as an indicator of the presence of sensitive receptors to environmental hazards. While health care facilities can also be an asset to mitigate negative environmental impacts, this positive function was not considered here because the presence of a health care facility in or around a community may not indicate that it is accessible or utilized by local residents [ 9 ].
We retrieved the dataset of locations of health care facilities from the Cal-Atlas website. In ArcGIS 9. We calculated the mean value of the percent area of each block group within the buffer zone, percent population in poverty, percent people older than 25 without a high school diploma, percent household that are considered linguistically isolated defined as without a member older than 14 speaks English fluently , percent people of color other than non-Hispanic White and percent population older than 60 and younger than 5.
Finally, we normalized the datasets by dividing its maximum value and multiplying by 10 to generate the SVI value for each block group. The formula of calculating SVI is shown below:. SVI inorm is the normalized social vulnerability index score for block group i. We constructed HI from data in low birth weight rate, years of potential life lost before age 65 YPLL65 and asthma hospitalization rate ages 0— These indicators were selected to represent conditions across the life span, and as those that can serve as proxies for overall health status.
Due to data availability, health data is based on zip code. We first normalized low birth weight rate, YPLL65 and asthma hospitalization rate ages 0—19 by dividing the data sets by its maximum value. For each zip code, the maximum value of the three health indicators was assigned as the value of health index. In this way, the health index was designed to reflect high value i.
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Then we converted the unit of analysis from zip code to block group. We generated a raster file in ArcGIS 9.
Each cell was assigned the value of HI from the zip code where the cell was located within. Second, we calculated the mean value of the HI from the raster file for each block group. Similarly to the way we converted tract-level NATA data to block group level, such conversion preserved the original precision of the source datasets. The precisions of NATA and health are still tract and zip code respectively.
The raster technique did not improve the precision of the original datasets.
The formula of calculating HI is shown below:. The boxplot presents the five statistics minimum, first quartile, median, third quartile and maximum within each category. Each category contains about cases. Arraying the CEHI and the SVI in one map produces a patchwork spatial pattern for the region in which where one lives has a profound effect on the environmental hazards in the proximate risk-scape Figure 3.
The map of CEVA reveals two important patterns. First, in urban areas with the highest levels of environmental hazards and social vulnerability, there is a patch work pattern of separate and unequal places. Second, significant overlap between environmental hazards and social vulnerability also occurs in many small, rural towns throughout the region where low-income communities and communities of color live amidst agricultural fields with intensive pesticide applications and non-agricultural industries such as power plants and waste disposal facilities.
Our analysis also examined the spatial distribution of health conditions with potential environmental influences. This is represented in Figure 4 below. Health conditions are caused by a wide range of factors, including genetics, individual behaviors, health care, and the social and physical environment.
This study addresses only the issue of social and physical environment, and therefore does not provide a comprehensive analysis of the breadth of causes of health problems in the region. However, using the Health Index, this study demonstrates that there are many thousands of people living with severe environmental hazards and high social vulnerability who are contending with a range of health problems that other research has shown to be correlated with environmental and social stressors.
This is seen especially in rural areas in the northwest section of the region, with some pockets along the foothills on the eastern side of the valley. To begin, our model is only as accurate as the source data sets. All the data sets we used are generated either on national or state scale and publicly available, which allows our model to be replicated. These data sets are the most reliable ones in their field. However, restricted by time and other resources, these data sets all have their own limitations, which are published along with the data sets.
For example, NATA does not consider diesel when assessing cancer risk.
The health impact from diesel may be addressed with transportation volume data, which is our next step for this study. Second, there are certain issues—including those correlated with severe health conditions—that lack data sets that are reliable and comprehensive in geographic scale. Water quality, for example, has long been an issue in the San Joaquin Valley region and could potentially have very important health impacts on residents.
While there is some water quality data available, it is not available at the census block group for the region as a whole, and therefore is not possible to incorporate this data into the CEHI. Third, the indices use data sources that cover a range of stages in the emissions, potential exposures, toxicity, and health risk process.
For example, the point source data e. The emissions data e. This is one of the factors that suggest that the CEHI is an under-estimate of the concentrations of environmental hazards in any given block group.
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Fourth, our model is limited by its geographic unit of analysis: the census block group, which is the smallest geographic unit that the U. Census Bureau aggregates certain demographic data. Therefore, CEHI and SVI calculated from our model are mostly likely to be accurate when we look at areas that are larger than a block group. Our model cannot provide much reference to areas smaller than a block group. The fifth limitation is related to using block group as the unit of analysis. By doing this, we assumed that people spend most of their time in or nearby their home and spread out evenly in a block group.
In fact, people constantly move and population densities often vary greatly within each block group. Using residential areas as the unit of analysis can address the variation of population densities. A final caution on the use of place-based analysis such as the CEHI is that it does have a limited ability to account for hazards that originate outside the block group e. The inclusion of the NATA data does partially address this. Finally, the indices are relative to the region and not absolute measures.
Relative indices present the regional patterns well but preclude being able to track progress over time, as conditions in individual places or the entire region may have declined. Because the SJV faces high incidence of environmental and social problems relative to the state and nation as a whole, the relative low or medium score of some areas compared with SJV can mask vulnerabilities.
More broadly it is important to state that the CEHI is not a formal risk assessment that quantifies the specific pollution exposures. We used metrics that measure emissions, potential exposures, as well as health risks. It should also be noted that there are a range of other data sets that were not included in this index because of challenges of data availability at the appropriate spatial scale, with a region-wide scope, or with reliable sources.
Therefore, the CEHI should be understood as a screening method, helping to identify places with higher relative degrees of environmental hazards compared to the region as a whole. Future improvements to the CEVA ought to include strategies to address the above limitations, with a priority on including air quality PM, Ozone , drinking water quality particularly nitrate and arsenic and transportation volume; integrating measures of pesticide and point source site emissions fate and toxicity; incorporation of bio-monitoring and health condition data to move beyond exposure to actual health impacts; attention to especially vulnerable populations such as children, seniors, prisoners, and undocumented residents; longitudinal studies to track the relationships between environmental hazards, social vulnerability factors, and health conditions overtime; and methods to track individual mobility and behaviors to better account for the multiple exposure pathways.
Despite the limitations of the CEVA method, it offers clear advantages by analyzing multiple factors involved in environment hazards and social vulnerability. Besides national air toxic assessment, CEVA includes other indicators of localized environmental hazards such as pesticide applications and point source pollutions sites. It goes beyond income and race when considering the social vulnerability of the residents by incorporating formal education, English language fluency, age, and in-patient residence into the model.
It also brings in health status as a reference to illustrate how the existing health problems may exacerbate the vulnerability to environmental hazards.
Given the focus of environmental justice on the disproportionate burdens of environmental hazards on the most vulnerable populations and places [ 16 ] it can be argued that the CEVA map Figure 2 reflects the spirit as well as the letter of the relevant laws. We recommend that these areas receive special consideration in permitting, monitoring, and enforcement actions, as well as investments in public participation, capacity building, and community economic development. For example, the Air District may use the CEVA to focus its resources, such as incentive funds, air quality monitoring, permitting reviews, enforcement, and public education and outreach in these most vulnerable areas [ 45 ].
Furthermore, she uses extensive ethnographic research and detailed interviews with activists, scientists, and public health practitioners to dispel the myth that pesticide induced illnesses are not mere accidents, but systemic regulatory failures. Harrison argues for various solutions as well as the need for a new form of environmental justice that can effectively protect vulnerable populations from pesticide exposure. The California Department of Pesticide Regulation Pesticide Illness Surveillance Program PISP evaluates reports of any known or suspected pesticide-induced illness before they are sent to the county agricultural commissioner for investigation.
The CDPR provides information regarding county agricultural commissioners that handle local pesticide use problems.