I did a web search to see what was returned when looking for the September 2020 San Franciso ‘orange sky’ event and purple air and the below link was one of the first returned
The Bay Area Just Turned Orange. All Eyes Are on PurpleAir
India, China and other regions come to mind also for more extreme air quality issues that could provide comparison and understanding on the efficacy of the network given a number of units and their locational density or placement.
In addition to sharing stories like this more directly with Purple Air users or potential users, it would be helpful if possibly these historically noteworthy events (say defined by some bounding box and time range) were also available as KML files, so we could visualize the event on a larger map scale with time-step controls or reference patterns in regards to pollutions sources or wind/circulation spread/decline over time. I or anyone could or maybe has done something like this with python scripting, etc. Thanks
I’ve often dreamed of something like this, too. I’d like to be able to have a “time slider” applied to the PurpleAir map that lets me just slide left and right, backwards and forwards in time, and see the monitors change color based on their readings at that time. I would LOVE this. I think that lots of people would like to share those animations, too. It would be mesmerizing to see the air flowing through a densely monitored area.
I took a try at this using the below python script which will take a group of csv files in a given folder and use the time and last column value in the air quality file to create a corresponding kml file, but forgot how poorly google earth handles a large number of points(say > 15k points). I downloaded all the map points at the below San Francisco map location from September 1 to September 20, 2020 when the ‘orange sky’ news event occured, it was around 13,000 files and took several hours, probably could have run this download part faster/better using the official API than the map download page. Using the map download page, would have been nice to be able to exclude empty files(where data does not exist historically at a platform) and filter say for only ‘A’ sensors (not redundant ‘B’) and ‘outside’. The kml file generated was around 100+ megabytes unzipped, several megabytes zipped as a kmz file and google earth became unusable trying to load/show it.
So the next visualization step I might try would be to reformat the output into a single csv file(time,lat,long,values) that I could load into QGIS and have QGIS run through the data and create an animated gif of the changing color point values for a region.
Above is an animated gif/legend of some of the purple air monitors for the San Francisco region during the ‘orange sky’ event on September 10, 2020. The gif loops from September 10 through 13(times are UTC) and file upload size is limited to 4 MB max. This was done using the below python script which produces this simple csv file , which is imported into QGIS for visualization. There’s probably a way to produce some nice animated maps similarly with using python and geospatial mapping libraries, etc, but don’t have an easy example to share at the moment.
Looking at the visualization, the downtown area seems to get the heavier smoke first and takes several days for the levels to gradually reduce. I split the values into 48 classes of about 5 points air quality each, the visualization could be improved with better color/legend breaks and a larger area to show larger wind/distribution patterns.
Placing my scripts and files also at this shared folder
from glob import glob
import sys
import csv
file = sys.argv[1]
f = open("purple_air_sf_2020.csv", "w")
f.write("time,lon,lat,aq\n")
file_cnt = 0
allFiles = glob(sys.argv[1]+"/*.csv")
for file in allFiles:
file_cnt = file_cnt+1
print (file_cnt)
print (file)
if ' B ' in file:
continue
if 'inside' in file:
continue
file_fmt = file.replace('(Outside)','')
file_fmt = file_fmt.replace('(exterior)','')
file_fmt = file_fmt.replace('(Evelyn)','')
file_fmt = file_fmt.replace('(1 of 5)','')
file_fmt = file_fmt.replace('(2 of 5)','')
file_fmt = file_fmt.replace('(3 of 5)','')
file_fmt = file_fmt.replace('(4 of 5)','')
file_fmt = file_fmt.replace('(5 of 5)','')
file_fmt = file_fmt.replace('(outdoor)','')
file_fmt = file_fmt.replace('(ish)','')
latlon = file_fmt.split('(')
print (latlon[2])
latlon = latlon[2].split(')')
#print (latlon)
(lat,lon) = latlon[0].split(' ')
with open(file) as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
line_count = 0
for row in csv_reader:
if line_count == 0:
#print(f'Column names are {", ".join(row)}')
line_count += 1
else:
#print(f'\t{row[0]} val {row[8]}')
line_count += 1
aq = row[8]
if aq == '' or float(aq) > 400:
continue
this_time = row[0].replace(' UTC','')
#this_time = this_time.replace(' ','T')
f.write(this_time+","+lon+","+lat+","+aq+"\n")
f.close()
