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Data Project: The air I breathe how polluted is it?

by bernt & torsten

I wanted to find out how polluted the air is around me, and if it had improved now when we work from home and there are fewer airplanes going past or landing or departing from Schiphol airport, I live close to the Schiphol Airport in Amsterdam.

I’m an asthmatic and I have lately noticed that my asthma has become less frequent. I was wondering if it could have anything to do with the Coronavirus outbreak and the restrictions we now live with?

PM10 and PM2.5

How would I get started, I need to find a dataset that I could analyze, I need to find a dataset which aggregates PM2.5, PM10, ozone (O3), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and black carbon (BC). These are pollutants that affect the air we breathe.

I was specifically interested in finding a dataset with the PM10 which is a particulate matter of 10 micrometres or less in diameter, PM2,5 is particulate matter 2.5 micrometres or less in diameter. PM2.5 is generally described as fine particles. By way of comparison, a human hair is about 100 micrometres, so roughly 40 fine particles could be placed on its width

Accordingly to experts PM2.5 poses the greatest health risk, the fine particles can get deep into the lungs and some may even get into the bloodstream. Exposure to these particles can affect a person’s lungs and heart. Coarse particles PM10 are of less concern, although they can irritate a person’s eyes, nose, and throat.

Fine particles can come from various sources. They include power plants, motor vehicles, airplanes, residential wood burning, forest fires, agricultural burning, volcanic eruptions, and dust storms. Some are emitted directly into the air, while others are formed when gases and particles interact with one another in the atmosphere.

DataSource

After a bit of research, I found that the OpenAQ offers datasets that have been collected in real-time from public government and research-grade sources. The public government and research sources do the hard work of measuring these data and publicly sharing them.

I also found out that the OpenAQ dataset is available in BigQuery, if you are using the Google Cloud Platform you can very quickly use Data Studio to make a report on that data.

I quickly started to inspect the OpenAQ dataset, just to understand the dataset. The schema of the table has all the fields I was after. It looked like this:

Field name	Type	Mode	Policy tags	Description
location	STRING	NULLABLE		The location where data was measured
city	STRING	NULLABLE		City containing location
country	STRING	NULLABLE		Country containing measurement in 2 letter ISO code
pollutant	STRING	NULLABLE		Name of the Pollutant being measured. Allowed values: PM25, PM10, SO2, NO2, O3, CO, BC
value	FLOAT	NULLABLE		The latest measured value for the pollutant
timestamp	TIMESTAMP	NULLABLE		The DateTime at which the pollutant was measured, in ISO 8601 format
unit	STRING	NULLABLE		The unit the value was measured in coded by UCUM Code
source_name	STRING	NULLABLE		Name of the source of the data
latitude	FLOAT	NULLABLE		Latitude in decimal degrees. Precision >3 decimal points.
longitude	FLOAT	NULLABLE		Longitude in decimal degrees. Precision >3 decimal points.
averaged_over_in_hours	FLOAT	NULLABLE		The number of hours the value was averaged over.

After that I create a query to filter out the data for the Amsterdam locations, there are several locations in Amsterdam as you can see from my query.

From the result of the query, I got several locations in Amsterdam where data is collected by RIVM. I can download the data source from the RIVM site, the same way that OpenAQ does it.

I’m not sure if you noticed it, in my BigQuery query result, the value reported in Amsterdam are negative values. Which to me did not look right, I found this explanation on how to determine the values from PM2.5 from the U.S. Environmental Protection Agency website.

PM 2.5	Air Quality Index	PM 2.5 Health Effects	Precautionary Actions
0 to 12.0	Good 0 to 50	Little to no risk.	None.
12.1 to 35.4	Moderate 51 to 100	Unusually sensitive individuals may experience respiratory symptoms.	Unusually sensitive people should consider reducing prolonged or heavy exertion.
35.5 to 55.4	Unhealthy for Sensitive Groups 101 to 150	Increasing likelihood of respiratory symptoms in sensitive individuals, aggravation of heart or lung disease and premature mortality in persons with cardiopulmonary disease and the elderly.	People with respiratory or heart disease, the elderly and children should limit prolonged exertion.
55.5 to 150.4	Unhealthy 151 to 200	Increased aggravation of heart or lung disease and premature mortality in persons with cardiopulmonary disease and the elderly; increased respiratory effects in general population.	People with respiratory or heart disease, the elderly and children should avoid prolonged exertion; everyone else should limit prolonged exertion.
150.5 to 250.4	Very Unhealthy 201 to 300	Significant aggravation of heart or lung disease and premature mortality in persons with cardiopulmonary disease and the elderly; significant increase in respiratory effects in general population.	People with respiratory or heart disease, the elderly and children should avoid any outdoor activity; everyone else should avoid prolonged exertion.
250.5 to 500.4	Hazardous 301 to 500	Serious aggravation of heart or lung disease and premature mortality in persons with cardiopulmonary disease and the elderly; serious risk of respiratory effects in the general population.	Everyone should avoid any outdoor exertion; people with respiratory or heart disease, the elderly and children should remain indoors.

The data I was querying from BigQuery does not have the correct results, it could be many reasons that the data is not showing correctly for the day I made the query. To check the data I went to the RIVM website luchtmeetnet to validate the data and it seems that there is some gap in the reported data some days in a week.

The location I was interested in that is nearest to where I live is Badhoevedorp-Sloterweg. What I read on that location is that the measuring station Badhoevedorp is classified by the RIVM as an unclassified type of location. A few people live at this location and there are quite some busy roads and the international Airport Schiphol in the immediate vicinity.

It also said that The Province of North Holland has commissioned the GGD Amsterdam to measure the air quality at this location – basically, they have outsourced the measure of the air quality.

I went through the data for the Badhoevedorp-Sloterweg and this is what the chart was showing me.

What it shows me is that the PM2.5 took a dive in February since that time it has climbed almost back to the value that was measured in January. What does that mean as we are still in the middle of the coronavirus, the values are almost back at normal, I have noticed that traffic is as before the coronavirus in my neighbourhood. Maybe where I live the air quality has not changed that much.

Maybe that dip in February helped my asthma, I’m not sure so I’m had to check if staying at home has affected my asthma.

In Your House

I do not have animals, cats, and dogs which are the normal house animals that do trigger asthma in me. Staying at home working from home, I’m not getting exposed to people that have pets at home, and maybe that is why I feel better.

The other day I had an asthma attack at home, nothing serious enough to give discomfort. I have been working from home for the last two months and have not been in contact with any people that have pets. It’s something at home that triggered asthma, I had now invested in a PM2.5 detector for home use.

I will collect data for a few months, and then analyze that data to see what results I get from that. I have suspected for some time that when the gas stove is used a lot I seem to get discomfort, now I have a tool to find out.

Conclusion

I will measure my inhouse pollution and when I have a big dataset I do some analysis and write up an article on those findings.