⚙️ How-To Toxic releases Maps & visualisation Notebook By · Updated

How to Map Toxic Release Facilities by Amount Released

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Problem statement

A map of toxic release facilities is easy to draw, a dot per facility from the coordinates in the Toxics Release Inventory, and hard to read: there are more than twenty thousand of them, and their releases range from a fraction of a pound to hundreds of millions of pounds. Sizing the dots by amount fixes the first problem and creates another, because a handful of mines would cover the map. This page totals each facility's 2024 releases, scales symbols on a log scale, colours them by industry and checks the points that overlap.

Quick answer

Total the facility's chemicals in pounds and make points from its coordinates:

t24["lb"] = pounds(t24)
fac = t24.groupby("TRIFD").agg(name=("FACILITY NAME", "first"), st=("ST", "first"), lb=("lb", "sum"),
                                lat=("LATITUDE", "first"), lon=("LONGITUDE", "first"), sector=("INDUSTRY SECTOR", "first"))
g = gpd.GeoDataFrame(fac, geometry=gpd.points_from_xy(fac.lon, fac.lat), crs=4269)
print(g.nlargest(5, "lb")[["name", "st", "sector", "lb"]].assign(lb=lambda d: (d.lb / 1e6).round(1)).to_string())
                                                                   name  st                  sector     lb
TRIFD                                                                                                     
99752RDDGP90MIL                                      RED DOG OPERATIONS  AK            Metal Mining  723.9
84006KNNCT12300  KENNECOTT UTAH COPPER MINE CONCENTRATORS & POWER PLANT  UT            Metal Mining  192.7
99801KNNCT13401                            HECLA GREENS CREEK MINING CO  AK            Metal Mining   87.6
7973WMDWYG8824F                                        MIDWAY GAS PLANT  TX  Natural Gas Processing   73.3
85235SRCNC64ASA   ASARCO LLC RAY COMPLEX/ HAYDEN SMELTER & CONCENTRATOR  AZ          Primary Metals   55.0

The largest are mines: Red Dog in Alaska released 723.9 million lb in 2024, Kennecott's Utah copper operations 192.7 million and Greens Creek in Alaska 87.6 million. A gas plant in Texas and a copper smelter in Arizona complete the top five.

Horizontal bars of the twelve facilities with the largest 2024 releases, metal mines in amber: Red Dog Operations in Alaska 723.9 million lb, Kennecott Utah Copper 192.7, Hecla Greens Creek 87.6 and Midway Gas Plant 73.3. Horizontal bars of the twelve facilities with the largest 2024 releases, metal mines in amber: Red Dog Operations in Alaska 723.9 million lb, Kennecott Utah Copper 192.7, Hecla Greens Creek 87.6 and Midway Gas Plant 73.3.
The largest releases come from mines.

Scaling circles to pounds

1. A range of eleven orders of magnitude

pos = g[g.lb > 0].lb
print(f"facilities releasing something: {len(pos):,}; smallest {pos.min():.4f} lb, median {pos.median():,.0f} lb, largest {pos.max():,.0f} lb")
facilities releasing something: 17,378; smallest 0.0000 lb, median 1,696 lb, largest 723,889,153 lb

17,378 facilities released something, from less than a ten-thousandth of a pound to 723,889,153 lb; the median is 1,696 lb. Proportional symbols on a linear scale would show one circle and a scatter of dots.

2. Circles by area, on a log scale

g["size"] = (np.log10(g.lb.clip(lower=1)) + 1) * 1.5
print(pd.cut(g.lb, [-1, 0, 1e3, 1e5, 1e6, 1e7, 1e9], labels=["0", "≤1k", "1k-100k", "100k-1M", "1M-10M", "10M+"]).value_counts(sort=False).to_string())
lb
0          4104
≤1k        7894
1k-100k    7686
100k-1M    1393
1M-10M      366
10M+         39

On a log scale the classes are usable: 7,894 facilities fall in the ≤1k lb class, 7,686 in 1k-100k, and 39 released more than 10 million lb. The 4,104 that released nothing get the smallest symbol rather than disappearing.

Bar chart of facilities by pounds released in 2024: 4,104 released nothing, 7,894 up to a thousand pounds, 7,686 up to a hundred thousand, 1,393, 366, and 39 above ten million. Bar chart of facilities by pounds released in 2024: 4,104 released nothing, 7,894 up to a thousand pounds, 7,686 up to a hundred thousand, 1,393, 366, and 39 above ten million.
Facilities by amount released, on a log scale of classes.

3. Facilities that sit on top of each other

dup = g.groupby(["lat", "lon"]).size()
print(f"locations shared by 2+ facilities: {(dup > 1).sum():,}, holding {dup[dup > 1].sum():,} facilities")
locations shared by 2+ facilities: 213, holding 472 facilities

213 locations hold two or more facilities, 472 in all. Sites with several operators report separately under one address, and their symbols stack.

By sector and by county

The data used on this page

The US EPA's Toxics Release Inventory basic data files for 2014 and 2024, public domain, downloaded on 16 October 2026 and saved on this site, because EPA revises past years when facilities resubmit. With them come the Census Bureau's Vintage 2025 county population estimates and 2024 county boundaries.

from pathlib import Path
import re
import zipfile

import geopandas as gpd
import numpy as np
import pandas as pd
import requests

Path("data").mkdir(exist_ok=True)
# EPA Toxics Release Inventory basic data files, reporting years 2014 and 2024, all states, xz-compressed and
# otherwise unchanged; Census Vintage 2025 county estimates and 2024 cartographic boundary counties.
# Downloaded 16 October 2026 and saved here, because EPA revises past years.
# Live: https://data.epa.gov/efservice/downloads/tri/mv_tri_basic_download/2024_US/csv (and 2014_US)
ARCHIVE = "epa-tri-2014-2024-2026-10-16.zip"
if not Path(f"data/{ARCHIVE}").exists():
    Path(f"data/{ARCHIVE}").write_bytes(requests.get(f"https://data.spatialworkflow.io/{ARCHIVE}", timeout=600).content)
Z = zipfile.ZipFile(f"data/{ARCHIVE}")


def tri(year):
    """One year's TRI file: a row per facility and chemical, columns numbered '1. YEAR' to '122. ...'."""
    d = pd.read_csv(Z.open(f"tri_{year}_us.csv.xz"), compression="xz", dtype=str, low_memory=False)
    d.columns = [re.sub(r"^\d+\.\s*", "", c) for c in d.columns]
    amounts = list(d.columns[d.columns.get_loc("5.1 - FUGITIVE AIR"):d.columns.get_loc("8.8 - ONE-TIME RELEASE") + 1])
    d[amounts + ["LATITUDE", "LONGITUDE"]] = d[amounts + ["LATITUDE", "LONGITUDE"]].apply(pd.to_numeric, errors="coerce")
    return d


def pounds(d, col="TOTAL RELEASES"):
    """A quantity in pounds: dioxin and dioxin-like compounds are reported in grams."""
    return d[col].where(d["UNIT OF MEASURE"] == "Pounds", d[col] / 453.59237)


t24 = tri(2024)
pop = pd.read_csv(Z.open("co-est2025-alldata.csv"), encoding="latin-1", dtype={"STATE": str, "COUNTY": str})
pop = pop[pop.SUMLEV == 50].assign(GEOID=lambda p: p.STATE + p.COUNTY)
Path("data/cb_2024_us_county_20m.zip").write_bytes(Z.read("cb_2024_us_county_20m.zip"))
counties = gpd.read_file("data/cb_2024_us_county_20m.zip")
print(f"TRI 2024: {len(t24):,} facility-chemical rows from {t24.TRIFD.nunique():,} facilities; {pounds(t24).sum() / 1e9:.2f} billion lb released")
TRI 2024: 77,295 facility-chemical rows from 21,482 facilities; 3.29 billion lb released

Example 1: the map coloured by sector

low = g[~g.st.isin(["AK", "HI", "PR", "GU", "VI", "MP", "AS"])].to_crs(5070)
top = low.groupby("sector").lb.sum().nlargest(5).index
print(low.assign(sector=low.sector.where(low.sector.isin(top), "other")).groupby("sector").agg(facilities=("lb", "size"), mlb=("lb", lambda s: round(s.sum() / 1e6, 1))).to_string())
ax = low.plot(column=low.sector.where(low.sector.isin(top), "other"), markersize=low["size"], alpha=0.6, legend=True, figsize=(11, 6))
ax.set_axis_off()
                    facilities    mlb
sector                               
Chemicals                 3369  448.7
Electric Utilities         358  178.9
Hazardous Waste            217  204.4
Metal Mining                82  505.3
Primary Metals            1375  344.3
other                    15874  762.0
Map of the lower 48 with facilities as circles sized on a log scale by pounds released and coloured by sector: metal mines in Nevada, Utah and Arizona, chemical plants along the Gulf Coast, primary metals in the Midwest. Map of the lower 48 with facilities as circles sized on a log scale by pounds released and coloured by sector: metal mines in Nevada, Utah and Arizona, chemical plants along the Gulf Coast, primary metals in the Midwest.
Facilities sized by pounds released in 2024 and coloured by sector.

Chemical plants are the most numerous of the big five sectors, 3,369 facilities releasing 448.7 million lb, while 82 metal mines in the lower 48 released 505.3 million. Mines cluster in Nevada, Utah and Arizona, chemical plants along the Gulf Coast.

Example 2: counties with the most facilities

j = gpd.sjoin(g.to_crs(4269), counties.to_crs(4269)[["GEOID", "NAME", "STUSPS", "geometry"]], predicate="within")
print(j.groupby(["NAME", "STUSPS"]).size().nlargest(5).to_string())
NAME         STUSPS
Harris       TX        358
Los Angeles  CA        307
Cook         IL        290
Maricopa     AZ        194
Cuyahoga     OH        126

Harris County, Texas, home of the Houston Ship Channel, has the most facilities, 358, then Los Angeles and Cook counties.