Cancer Alley in Louisiana is an 85-mile-long region with a concentration of petrochemical and industrial plants along the Mississippi River, contributing to the area’s high incidence of cancer and other illnesses. The area’s population of approximately 45,000 residents is predominantly low-income and African Americans, and they have been advocating for the reduction of emissions from these facilities that pollute the air, water, and soil in their surrounding homes.

The Louisiana Department of Environmental Quality (LDEQ) emissions inventory dataset from 1991 to 2022 provides information about the amount and type of pollutants released by industrial facilities in Louisiana. This dataset includes facility location and emissions data in pounds or tons per year. Our team analyzed this dataset to better understand the impact of emissions from these facilities on the health and well-being of the local populace, as opposed to the economic benefits touted by these businesses.

How are chemical emissions quantified?

The LDEQ employs multiple methods to quantify emissions inventory in their public datasets, including direct measurement, calculation, and modeling:

Data limitations

The LDEQ has identified limitations in emissions reports, which can impact the accuracy of measuring pollution from companies.

Furthermore, emissions reports only cover a limited range of pollutants, which can exclude other harmful substances. These limitations may have adverse public health and economic consequences and are particularly significant in Louisiana, which has a high concentration of industrial facilities emitting pollutants.

Additionally, other limitations our team found within this dataset are the following:

For more detailed information about these limitations, refer to the LEDQ document (link).

Cancer causing chemicals

The National Institutes of Health (NIH), a division of the U.S. Department of Health and Human Services, conducts vital medical research to enhance health and extend lives. The NIH recognizes various carcinogenic pollutants, including:

Additionally, the National Toxicology Program (NTP), a federal interagency program responsible for evaluating the potential health hazards of chemicals and substances in the environment, has identified several substances as known or reasonably anticipated human carcinogens. These substances include the following:

It is important to note that the risks associated with exposure to these chemicals can vary depending on the dose, duration, and other factors. Cancer is a complex illness with numerous causes, and exposure to these substances is just one of several factors that may contribute to cancer risk.

Project focus: Ascension and Calcasieu counties

According to the Environmental Integrity Project’s (EIP) 2018 report, more than a third (34%) of total toxicity-weighted air emissions in the United States originated from only ten counties. Interestingly, Ascension and Calcasieu, located in Louisiana, were among those counties. As a result of this discovery, we conducted an examination of the various pollutants emitted by facilities in these two counties from the LDEQ dataset.

Following an analysis of the initial dataset, our attention was directed towards the 15 chemical pollutants known to contribute to cancer based on our research, out of the original 173:

It is worth noting that while the original dataset spanned from 1991 to the present, we chose to concentrate on the period from 2000 to 2018 to examine trends in accordance with the EIP’s 2018 report. Thus, we opted to generate a fresh sub-dataset that highlights the 15 chemical pollutants that have been emitted by different facilities in Ascension and Calcasieu. Furthermore, any missing data was treated as a contribution of zero. Finally, we decided to display and group the different pollutants in 6 different groups for simplicity and visual purposes:

# Reusable Variables
graph_colors <- c("#999999", 
                  "#E69F00", 
                  "#56B4E9", 
                  "#009E73", 
                  "#F0E442", 
                  "#0072B2")
legend_variables_labels <- c("Others",
                             "Benzene",
                             "Butadiene",
                             "Ethylene",
                             "Formaldehyde",
                             "Vinyl")
# Select pollutants/variables that contribute the most to cancer
data_set_before_2015 <- data_set_before_2015 %>% 
  select(REPORTING_YEAR,
        AI_NAME, 
        PARISH_OR_COUNTY_DESC,
        `1,3-Butadiene`,
        `Arsenic (and compounds)`,
        `Asbestos`,
        `Benzene`,
        `Benzidine`,
        `Beryllium (Table 51.1)`,
        `Cadmium (and compounds)`,
        `Chromium VI (and compounds)`,
        `Ethylene oxide`,
        `Formaldehyde`,
        `Nickel (and compounds)`,
        `Nickel (refinery dust)`,
        `Sulfuric acid`,
        `Trichloroethylene`,
        `Vinyl chloride`
        )

# Select pollutants/variables that contribute the most to cancer
data_set_after_2015 <- data_set_after_2015 %>% 
  select(REPORTING_YEAR,
        AI_NAME, 
        PARISH_OR_COUNTY_DESC,
        `1,3-Butadiene`,
        `Arsenic (and compounds)`,
        `Asbestos`,
        `Benzene`,
        `Benzidine`,
        `Beryllium (Table 51.1)`,
        `Cadmium (and compounds)`,
        `Chromium VI (and compounds)`,
        `Ethylene oxide`,
        `Formaldehyde`,
        `Nickel (and compounds)`,
        `Nickel (refinery dust)`,
        `Sulfuric acid`,
        `Trichloroethylene`,
        `Vinyl chloride`
        )
# Join both tables to have a single data set
data_set <- data_set_before_2015 %>% 
  full_join(data_set_after_2015, 
            by = join_by(REPORTING_YEAR,
                        AI_NAME, 
                        PARISH_OR_COUNTY_DESC,
                        `1,3-Butadiene`,
                        `Arsenic (and compounds)`,
                        `Asbestos`,
                        `Benzene`,
                        `Benzidine`,
                        `Beryllium (Table 51.1)`,
                        `Cadmium (and compounds)`,
                        `Chromium VI (and compounds)`,
                        `Ethylene oxide`,
                        `Formaldehyde`,
                        `Nickel (and compounds)`,
                        `Nickel (refinery dust)`,
                        `Sulfuric acid`,
                        `Trichloroethylene`,
                        `Vinyl chloride`)) %>% 
  arrange(AI_NAME)
# Creating `ascension` table and filter data based on `Ascension` county
ascension <- data_set %>% 
  filter(PARISH_OR_COUNTY_DESC == "Ascension") %>% 
  arrange(AI_NAME)

# Creating `calcasieu` table and filter data based on `Calcasieu` county
calcasieu <- data_set %>% 
  filter(PARISH_OR_COUNTY_DESC == "Calcasieu") %>% 
  arrange(AI_NAME)
# Selecting facilities that contributed to the production of at least one pollutant
ascension2 <- ascension %>% filter(rowSums(is.na(ascension)) != ncol(ascension) - 3)

# Remove Columns by Index
ascension2 <- ascension2[-c(2:3)]


calcasieu2 <- calcasieu %>% filter(rowSums(is.na(calcasieu)) != ncol(calcasieu) - 3)
calcasieu2 <- calcasieu2[-c(2:3)]

# Replace NA values with 0
ascension2[is.na(ascension2)] <- 0
calcasieu2[is.na(calcasieu2)] <- 0
# Table that holds the Ascension total number of facilities by year 
ascension_total_facilities <- ascension %>% 
  filter(REPORTING_YEAR >= 2000 & REPORTING_YEAR <= 2018) %>% 
  group_by(REPORTING_YEAR) %>% 
  summarise(TOTAL_FACILITIES = n() ) 

# Table that holds the Ascension total number of facilities that contributes to at least one of the selected cancer pollutant by year (2000-2018)
ascension_pollutant_facilities <- ascension2 %>% 
  filter(REPORTING_YEAR >= 2000 & REPORTING_YEAR <= 2018) %>% 
  group_by(REPORTING_YEAR) %>% 
  summarise(POLLUTANTL_FACILITIES = n() ) 

# Table that holds Ascension pollutant and non-pollutant facilities
ascension_facilities <- ascension_total_facilities %>% 
  left_join(ascension_pollutant_facilities, 
            join_by(REPORTING_YEAR))

Graphs & results

Total number of facilities in Ascension and Calcasiue counties

Table 1 and table 2 display a bar graph that classifies the overall count of facilities in Ascension Parish and Calcasieu counties based on their pollutant and non-pollutant status from 2000 to 2018, grouped by year. In this particular study, a facility is identified as a pollutant if it has contributed to at least one of the 15 pollutants under our study. Otherwise, any facility that did not release any of the previously mentioned pollutants is considered non-pollutant.

# Plotting Ascension findings
ascension_facilities %>% mutate(NON_POLLUTANT_FACILITIES = TOTAL_FACILITIES - POLLUTANTL_FACILITIES) %>% 
  select(REPORTING_YEAR,
         NON_POLLUTANT_FACILITIES,
         POLLUTANTL_FACILITIES) %>% 
  pivot_longer(cols = 2:3,
               names_to = "type",
               values_to = "total") %>% 
  ggplot(aes(x = factor(REPORTING_YEAR), 
             y = total, 
             fill = type)) +
  geom_bar(stat = "identity") +
  labs(title = "Total Number of Facilities in Ascension (2000-2018)",
       x = "Year",
       y = "Total Facilities",
       caption = str_wrap("Graph 1. Displays a bar graph that categorizes the total number of facilities in Ascension Parish according to their pollutant and non-pollutant status for the years 2000-2018 and grouped by year.")) + 
 theme(axis.text.x = element_text(angle = 90, 
                                   vjust = 0.5, 
                                   hjust = 1)) +
  scale_fill_manual(name="",
                    values=c("#999999", 
                             "#E69F00"), 
                    labels = c("Non-pollutant facilities", 
                               "Pollutant Facilities"))

This graph provides a visual representation of Ascension Parish’s facilities, distinguishing between those that produce our selected pollutants and those that do not. The following observations can be made from the graph:

  • The number of facilities increased over the years
  • Pollutant facilities also increased over the same period
  • Non-pollutant facilities also saw a slight increased
# Table that holds the Calcasieu total number of facilities by year 
calcasieu_total_facilities <- calcasieu %>% 
  filter(REPORTING_YEAR >= 2000 & REPORTING_YEAR <= 2018) %>% 
  group_by(REPORTING_YEAR) %>% 
  summarise(TOTAL_FACILITIES = n() ) 

# Table that holds the Calcasieu total number of facilities that contributes to at least one cancer pollutant by year
calcasieu_pollutant_facilities <- calcasieu2 %>% 
  filter(REPORTING_YEAR >= 2000 & REPORTING_YEAR <= 2018) %>% 
  group_by(REPORTING_YEAR) %>% 
  summarise(POLLUTANTL_FACILITIES = n() ) 

# Table that holds Calcasieu pollutant and non-pollutant facilities
calcasieu_facilities <- calcasieu_total_facilities %>% 
  left_join(ascension_pollutant_facilities, 
            join_by(REPORTING_YEAR))
# Plotting Calcasieu findings
calcasieu_facilities %>% mutate(NON_POLLUTANT_FACILITIES = TOTAL_FACILITIES - POLLUTANTL_FACILITIES) %>% 
  select(REPORTING_YEAR, 
         POLLUTANTL_FACILITIES, 
         NON_POLLUTANT_FACILITIES) %>% 
  pivot_longer(cols = 2:3,
               names_to = "type",
               values_to = "total") %>% 
  ggplot(aes(x = factor(REPORTING_YEAR), 
             y = total, 
             fill = type)) +
  geom_bar(stat = "identity") +
  labs(title = "Total Number of Facilities in Calcasieu (2000-2018)",
       x = "Year",
       y = "Total Facilities",
       caption = str_wrap("Graph 2. Displays a bar graph that categorizes the total number of facilities in Calcasieu County according to their pollutant and non-pollutant status for the years 2000-2018 and grouped by year.")) + 
 theme(axis.text.x = element_text(angle = 90, 
                                   vjust = 0.5, 
                                   hjust = 1)) +
  scale_fill_manual(name="",
                    values=c("#999999", 
                             "#E69F00"), 
                    labels = c("Non-pollutant facilities", 
                               "Pollutant Facilities"))

Graph 2, similar to Graph 1, presents a visual representation of Calcasieu’s establishments, differentiating between those that generate pollutants and those that do not.

The following observations can be made from this graph:

  • There was a significant rise in the number of establishments from 2000 to 2005.
  • In 2006, the number of establishments decreased significantly, falling from over 125 in 2005 to under 50 in 2006.
  • After 2006, the total number of establishments slightly declined over time, but it can be regarded as relatively stable.
  • Compared to non-pollutant establishments, the total number of pollutant establishments displayed a stable trend from 2000 to 2006.
  • Despite the overall reduction in the number of establishments after 2006, there was a minor increase in the number of pollutant establishments, resulting in a decrease in the number of non-pollutant establishments.

Expanding on our previous points, it is clear that there was a significant drop in the number of facilities in Calcasieu Parish, Louisiana after 2005. However, it’s worth noting that the chemical industry has faced a number of challenges in recent years, including changes in market conditions and regulatory requirements, as well as the impacts of natural disasters such as experiencing significant impacts from Hurricane Rita, which made landfall in the area less than a month after Hurricane Katrina in 2005. This natural disaster can be considered one of the main reasons of this considerable drop.

Rita was a Category 3 hurricane that caused widespread damage and power outages in southwest Louisiana, including Calcasieu Parish. The storm surge and high winds caused significant damage to buildings and infrastructure, and many residents were displaced from their homes for weeks or months.

For example, the Citgo refinery in Lake Charles, which is located in Calcasieu Parish, had to shut down for several weeks after the storm due to flooding and power outages. However, many other companies in the area were able to quickly resume operations and continue producing essential products such as gasoline and other fuels to support recovery efforts in the region.

Another potential factor that may have contributed to a decline in the number of facilities in the area is increasing competition from other regions, particularly those with access to cheaper feed stocks or lower operating costs. For example, the shale gas boom in Texas and other parts of the country has led to significant investments in chemical production facilities in those areas.

It is likely that a combination of factors has contributed to changes in the number and composition of chemical facilities in Calcasieu Parish and other industrial regions over time.

Overall, Ascension and Calcasieu counties graphs helps us understand the ratio over time of facilities that contribute to the selected pollutants.This information is useful for identifying areas of concern regarding pollution control and prevention efforts and gaining an understanding of the facilities’ spatial distribution.