Sunday, April 2, 2017

Air Quality In Poland #08 - Is it so bad near SlRybniBorki?

In previous post we found that measurement station SlRybniBorki won two times in terms of extreme values across year 2015 for main pollutants. Can we tell more about actual situation there?

If we check Chief Inspectorate for Environmental Protection web page, we can find documentation, which says, that each of main seven pollutants can be divided into one of six categories: "Very good", "Good", "Moderate", "Sufficient", "Bad" and "Very bad". First category says that amount of pollutant is very small, last says that measurement just went above last threshold and situation is indeed very bad. To categorize each pollutant I wrote simple function (example for C6H6):

 def C6H6qual (value):  
   if (value >= 0.0 and value <= 5.0):  
     return "1 Very good"  
   elif (value > 5.0 and value <= 10.0):  
     return "2 Good"  
   elif (value > 10.0 and value <= 15.0):  
     return "3 Moderate"  
   elif (value > 15.0 and value <= 20.0):  
     return "4 Sufficient"  
   elif (value > 20.0 and value <= 50.0):  
     return "5 Bad"  
   elif (value > 50.0):  
     return "6 Very bad"  
     return value  

and applied it on previously cross sectioned data frame

 reducedDataFrame = bigDataFrame['2015-01-01 00:00:00':'2015-12-31 23:00:00'].loc[(slice(None),pollutedPlaces), :]  

In order to estimate severity of pollution I grouped data points by station name, counted occurrences of each category and then divided it by total number of measurement point times 100. After this procedure I received series of measurement stations with percentage of data points assigned to each category:

 for pollutant in bigDataFrame.columns:  
   reducedDataFrame[pollutant+".desc"] = reducedDataFrame[pollutant].apply(lambda x: globals()[pollutant+"qual"](x))  
   tmpseries = reducedDataFrame.groupby(level="Station")[pollutant+".desc"].value_counts(dropna = False).apply(lambda x: (x/float(hours))*100)  
   descriptiveFrame = pd.concat([descriptiveFrame, tmpseries], axis=1)  

So what are the results?

It looks like over 3% of measurements of PM10 near SlRybniBorki falls into worst possible category. It means that there was more or less 260 hours when no one should be in open air near that station. This is crazy. And if we check address of that station we will find Rybnik, ul. Borki 37 d. As you can see on the map below, there is quite big park nearby, but it doesn't help much. If I would have to put my bets on source of pollution, I would point to power plant on north of there. What is pretty sad, you can see that there is Hospital located in direct neighborhood of most polluted place in Poland. Is it sealed constantly, so patients are not loosing their health just by breathing?

And if you assume that overall quality of air equals worst quality in that station for that time, those numbers can be even worst. But that is the material for next post. Stay tuned!

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