One of the most important elements of public health is regulatory interventions that yield clean air. In late 2016, we await the air quality crisis of the Delhi winter with trepidation. A few attempts at solving the problem have begun. The Government of Delhi experimented with an odd-even policy to regulate traffic between 1 January 2016 to 15 January 2016, and then between 15 April 2016 to 22 April 2016. The results of these experiments have been mixed [here and here].
What you measure is what you can manage. Only when we are able to marshal evidence in a systematic way about the extent and nature of the problem, will we be able to design and deliver a response. The measurement of air pollution in Delhi has begun on a small scale. In this post, we describe patterns seen in the available data.
Why is PM 2.5 a good measure?
There are many pollutants in the air such as carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO2), ozone (O3). The worst among these is small particulate matter, or PM 2.5, which are a mixture of solid and liquid droplets floating in the air whose diameters are less than 2.5 micrometers. These fine particles are produced from all types of combustion, including motor vehicles and power plants and some industrial processes.
The health impact from pollution is a complex transform of exposure to all pollutants. However, of the pollutants, PM 2.5 particles are considered the most harmful as they are able to enter deep into the respiratory tract, reaching the lungs. This can cause short-term health effects such as eye, nose, throat and lung irritation, coughing, sneezing, runny nose and shortness of breath, and in the long-term can affect lung function and worsen medical conditions such as asthma and heart disease. We, therefore, narrow our attention to the measure of PM 2.5. The unit of measurement of PM 2.5 is µg/m3 and the breakpoints of raw PM 2.5 values by the US Environmental Protection Agency are the following:
|24-hr PM 2.5||AQI Categories||Health Effects Statements|
|12.1-35.4||Moderate||Respiratory symptoms possible in unusually sensitive individuals,
possible aggravation of heart or lung disease in people with cardiopulmonary
and older adults.
|Increasing likelihood of respiratory symptoms in sensitive individuals, aggravation
of heart or lung disease and premature mortality in people with cardiopulmonary
disease and older adults.
|55.5-150.4||Unhealthy||Increased aggravation of heart or lung disease and premature mortality in
people with cardiopulmonary disease and older adults; increased respiratory
effects in general population.
|150.5-250.4||Very Unhealthy||Significant aggravation of heart or lung disease and premature mortality in
people with cardiopulmonary disease and older adults; significant increase in
respiratory effects in general population
|250.5-500||Hazardous||Serious aggravation of heart or lung disease and prematuremortality in people
with cardiopulmonary disease and older adults; serious risk of respiratory effects
in general population.
We fetch raw PM 2.5 values from two data sources on pollution in Delhi. The first is put out by the US Embassy based in Chanakyapuri. In addition, the Central Pollution Control Board also puts out real time data for various locations across India. We select 4 locations which provided us with the most consistent dataset. This gives us a total of 5 locations for which we have data:
- R K Puram
- Punjabi Bagh
- Mandir Marg
- US Embassy (Chanakyapuri)
- Anand Vihar
We use hourly data from the locations mentioned above for a time period from January 2013 to October 2016. It should be noted that values are missing from certain sections of the data. These missing observations are excluded from our analysis.
Drawing upon the Chinese experience, it's interesting to ask: Do the Indian government sources tally with the US Embassy data? We can't say, as there is no measurement for a location near the US Embassy by the CPCB.
Dimensions of variation
These are three types variations seen in PM 2.5.
|Figure 1: Variation by time of day|
Time Effect: Figure 1 above shows the variation in hourly pollution levels during different days of a week. Darker colors represent increased PM 2.5 matter in the air. We see that the pollution levels are low during the day, but start increasing post 6 p.m. and remain elevated till 9 a.m. of the next day. The average PM 2.5 concentration from 6 p.m. to 9 a.m. is 140 µg/m3, whereas the average PM 2.5 concentration from 9 a.m. to 6p.m. is 108 µg/m3. PM 2.5 levels in the range of 101-200 can cause breathing discomfort to anyone with prolonged exposure to the air during these times. This graph suggests that a measure that restricts traffic during the day such as the odd-even policy is unlikely to be as effective as a measure that restricts emissions at night.
|Figure 2: Variation by month|
Month Effect : Figure 2 shows the hourly variation in pollution levels during different months of the year. Note that the scale for this figure is different from that used in Figure 1. The monsoon months have the lowest levels of PM 2.5 particulate matter. Larger particles are settled in few hours due to gravity, but smaller particles such as PM 2.5 are removed by precipitation. Winters have the highest levels of PM2.5 matter in the air, on account of low wind speed and high relative humidity. PM 2.5 concentration reaches above 200 in the winter months, which can cause respiratory illness to people on prolonged exposure and puts people with respiratory illness, and heart disease on a far greater risk.
|Figure 3: Variation by location|
Location Effect: Figure 3 shows the hourly variation in pollution levels at the five locations where instruments are available. Chanakyapuri seems to perform better than other areas of Delhi, in terms of PM 2.5 particulate matter. Anand Vihar has the highest pollution levels amongst the 5 different locations, and has severe levels of air pollution in the night. This can cause respiratory impact even on healthy people, and serious health impacts on people with lung/heart diseases.
Thus, we see that there is a strong location effect on pollution levels. This can be due to the varying population densities of these locations as well as the proximity to industries etc. This could lead to location-specific policy initiatives such as closing down factories or modifying vehicular traffic.
Data and R code.
Dhananjay Ghei and Arjun Gupta are researchers at the National Institute of Public Finance and Policy. Renuka Sane is an academic at the Indian Statistical Institute, Delhi Centre.