😶‍🌫️ Utah Air Quality#

Demonstrates the following:

Getting timeseries and latest data

Plotting with custom color scale

Plotting with Polars built-in plotting (Altair)

Making a summary table with Great Tables

[2]:

from datetime import datetime

import matplotlib.pyplot as plt
import polars as pl
import seaborn as sns
from great_tables import GT
from herbie.toolbox import EasyMap
from matplotlib.colors import BoundaryNorm, LinearSegmentedColormap, ListedColormap

import synoptic

Ozone map#

Create a custom colormap for ozone

[3]:

colors = [
    "#00e400",  # Green   - Good
    "#ffff00",  # Yellow  - Moderate
    "#ff7e00",  # Orange  - Sensitive Groups
    "#ff0000",  # Red     - Unhealthy
    "#99004c",  # Purple  - Hazardous
    "#4c0026",  # Maroon  - Very Unhealthy
]
bounds = [0, 55, 71, 86, 106, 201, 300]
ozone_cmap = LinearSegmentedColormap.from_list("ozone", colors, N=len(colors))
norm = BoundaryNorm(bounds, ozone_cmap.N)
ozone_cmap

[3]:

ozone

under

bad

over

Get Ozone air quality data at a specific time

[4]:

df = synoptic.NearestTime(
    attime=datetime(2024, 7, 23, 18),
    within="30m",
    network=9,  # Utah's Division of Air Quality
    vars="ozone_concentration",
).df()
df = df.filter(sensor_index=1).sort("value", descending=True)
df

🚚💨 Speedy delivery from Synoptic's nearesttime service.
📦 Received data from 21 stations.

[4]:

shape: (19, 20)

date_time	variable	sensor_index	is_derived	value	units	id	stid	name	elevation	latitude	longitude	mnet_id	state	timezone	elev_dem	period_of_record_start	period_of_record_end	is_restricted	is_active
datetime[μs, UTC]	str	u32	bool	f64	str	u32	str	str	f64	f64	f64	u32	str	str	f64	datetime[μs, UTC]	datetime[μs, UTC]	bool	bool
2024-07-23 18:00:00 UTC	"ozone_concentration"	1	false	77.0	"ppb"	220329	"QUURB"	"UofU Red Butte"	4996.0	40.76652	-111.82836	9	"UT"	"America/Denver"	null	2023-09-01 18:00:00 UTC	2024-11-14 03:00:00 UTC	false	true
2024-07-23 18:00:00 UTC	"ozone_concentration"	1	false	75.0	"ppb"	31846	"QRP"	"Rose Park"	4232.0	40.7955	-111.9309	9	"UT"	"America/Denver"	4225.7	2016-12-07 16:10:00 UTC	2024-11-14 03:00:00 UTC	false	true
2024-07-23 18:00:00 UTC	"ozone_concentration"	1	false	73.0	"ppb"	10330	"QBV"	"Bountiful - Viewmont"	4304.0	40.9029	-111.8844	9	"UT"	"America/Denver"	4297.9	2004-06-09 00:00:00 UTC	2024-11-14 03:00:00 UTC	false	true
2024-07-23 18:00:00 UTC	"ozone_concentration"	1	false	73.0	"ppb"	31843	"QP2"	"Price"	5682.0	39.5958	-110.77	9	"UT"	"America/Denver"	5695.5	2011-11-01 00:00:00 UTC	2024-11-14 03:00:00 UTC	false	true
2024-07-23 18:00:00 UTC	"ozone_concentration"	1	false	70.0	"ppb"	152942	"QUTTC"	"Utah Technical Center"	4232.0	40.7771	-111.945	9	"UT"	"America/Denver"	4232.3	2020-06-29 19:12:00 UTC	2024-11-14 03:00:00 UTC	false	true
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
2024-07-23 18:00:00 UTC	"ozone_concentration"	1	false	57.0	"ppb"	47907	"QSM"	"Smithfield"	4508.0	41.84278	-111.85194	9	"UT"	"America/Denver"	4507.9	2015-12-04 19:29:00 UTC	2024-11-14 03:00:00 UTC	false	true
2024-07-23 18:00:00 UTC	"ozone_concentration"	1	false	57.0	"ppb"	70743	"QCV"	"Copperview"	4409.0	40.59806	-111.89417	9	"UT"	"America/Denver"	4409.4	2018-05-28 02:33:00 UTC	2024-11-14 03:00:00 UTC	false	true
2024-07-23 18:00:00 UTC	"ozone_concentration"	1	false	54.0	"ppb"	584	"QEN"	"Enoch"	5558.0	37.74745	-113.05552	9	"UT"	"America/Denver"	5551.2	2018-01-24 20:05:00 UTC	2024-11-14 03:00:00 UTC	false	true
2024-07-23 18:00:00 UTC	"ozone_concentration"	1	false	53.0	"ppb"	34586	"QHU"	"Hurricane"	3245.0	37.179	-113.3052	9	"UT"	"America/Denver"	3261.2	2012-09-07 00:00:00 UTC	2024-11-14 03:00:00 UTC	false	true
2024-07-23 18:00:00 UTC	"ozone_concentration"	1	false	53.0	"ppb"	87682	"QNR"	"Near Road"	4282.0	40.66284	-111.9018	9	"UT"	"America/Denver"	4278.2	2019-01-07 22:17:00 UTC	2024-11-14 03:00:00 UTC	false	true

[5]:

ax = EasyMap(figsize=(8, 8)).COUNTIES().STATES(linewidth=2, alpha=1).ROADS().ax

art = ax.scatter(
    df["longitude"],
    df["latitude"],
    c=df["value"],
    s=df["value"] ** 1.3,  # scale the sizes a little bigger
    cmap=ozone_cmap,
    norm=norm,
    ec="k",
    lw=0.5,
    zorder=10,
)

plt.colorbar(
    art, ax=ax, label="Ozone Concentration (ppb)", extend="max", pad=0.01, shrink=0.5
)

ax.set_title("Utah Ozone Concentration", loc="left", fontweight="bold")
ax.set_title(f'{df['date_time'].max():%H:%M UTC %d %b %Y}', loc="right")

ax.set_extent((-114.25, -108.75, 36.75, 42.25))

../../_images/user_guide_gallery_utah-air-quality_6_0.png

Ozone timeseries#

[6]:

df = synoptic.TimeSeries(
    start=datetime(2024, 7, 23, 21),
    end=datetime(2024, 7, 25, 21),
    network=9,  # Utah's Division of Air Quality
    vars="ozone_concentration",
).df()

# Datetimes as local timezone
df = df.synoptic.with_local_timezone()

# 3h rolling mean
df = df.rolling(
    "date_time", period="3h", group_by=pl.col("stid") + " - " + pl.col("name")
).agg(pl.col("value").mean())

# Plot
df.plot.line(x="date_time", y="value", color="stid").properties(
    title="Utah Air Quality", width=600
)

🚚💨 Speedy delivery from Synoptic's timeseries service.
📦 Received data from 21 stations.

[6]:

PM 2.5 table#

[32]:

df = synoptic.NearestTime(
    attime=datetime(2023, 12, 19, 21),
    within="30m",
    network=9,  # Utah's Division of Air Quality
    vars="PM_25_concentration",
).df()
df = df.filter(sensor_index=1).sort("value", descending=True)
df.head()

🚚💨 Speedy delivery from Synoptic's nearesttime service.
📦 Received data from 22 stations.

[32]:

shape: (5, 20)

date_time	variable	sensor_index	is_derived	value	units	id	stid	name	elevation	latitude	longitude	mnet_id	state	timezone	elev_dem	period_of_record_start	period_of_record_end	is_restricted	is_active
datetime[μs, UTC]	str	u32	bool	f64	str	u32	str	str	f64	f64	f64	u32	str	str	f64	datetime[μs, UTC]	datetime[μs, UTC]	bool	bool
2023-12-19 21:00:00 UTC	"PM_25_concentration"	1	false	42.1	"ug/m3"	87682	"QNR"	"Near Road"	4282.0	40.66284	-111.9018	9	"UT"	"America/Denver"	4278.2	2019-01-07 22:17:00 UTC	2024-11-14 03:00:00 UTC	false	true
2023-12-19 21:00:00 UTC	"PM_25_concentration"	1	false	31.6	"ug/m3"	589	"QHW"	"Hawthorne"	4301.0	40.7335	-111.8717	9	"UT"	"America/Denver"	4294.6	1997-03-28 00:00:00 UTC	2024-11-14 03:00:00 UTC	false	true
2023-12-19 21:00:00 UTC	"PM_25_concentration"	1	false	29.5	"ug/m3"	46466	"QED"	"Erda"	4334.0	40.60056	-112.355	9	"UT"	"America/Denver"	4327.4	2015-06-24 20:34:00 UTC	2024-11-14 03:00:00 UTC	false	true
2023-12-19 21:00:00 UTC	"PM_25_concentration"	1	false	29.5	"ug/m3"	152942	"QUTTC"	"Utah Technical Center"	4232.0	40.7771	-111.945	9	"UT"	"America/Denver"	4232.3	2020-06-29 19:12:00 UTC	2024-11-14 03:00:00 UTC	false	true
2023-12-19 21:00:00 UTC	"PM_25_concentration"	1	false	28.4	"ug/m3"	164230	"QLP"	"Lake Park"	4262.0	40.70987	-112.00866	9	"UT"	"America/Denver"	4252.0	2021-05-07 18:26:00 UTC	2024-11-14 03:00:00 UTC	false	true

[31]:

(
    GT(
        df.select("value", "stid", "name", "elevation")
        .sort("value", descending=True)
        .head(10)
    )
    .tab_header(
        title="Utah PM 2.5 Concentration (10 Highest)",
        subtitle=f"{df['date_time'].max():%H:%M UTC %d %b %Y}",
    )
    .fmt_number(columns="elevation", compact=True)
    .tab_spanner(label="Location", columns=["stid", "name", "elevation"])
    .cols_label(value="ug/m3", stid="ID", name="Name", elevation="Elevation (ft)")
)

[31]:

ug/m3	Location
Utah PM 2.5 Concentration (10 Highest)
21:00 UTC 19 Dec 2023
ug/m3	ID	Name	Elevation (ft)
42.1	QNR	Near Road	4.28K
31.6	QHW	Hawthorne	4.30K
29.5	QED	Erda	4.33K
29.5	QUTTC	Utah Technical Center	4.23K
28.4	QLP	Lake Park	4.26K
25.1	QBV	Bountiful - Viewmont	4.30K
24.4	QIP	Inland Port	4.23K
22.2	QBR	Brigham City	4.38K
22.1	QRP	Rose Park	4.23K
21.4	QSM	Smithfield	4.51K

😶‍🌫️ Utah Air Quality#

Ozone map#

Ozone timeseries#

PM 2.5 table#

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