Recent studies have demonstrated that the life-expectancy of people residing in the Indo-Gangetic plains is reduced by six years1 and living in some of India’s cities is equivalent to smoking 44 cigarettes/ day2 – all thanks to air pollution!
What is being done about it?
Well, various policies have been proposed in the recent past. In 2016, the Odd-Even policy was implemented in New Delhi (most polluted city in the world after Beijing) with the aim to cut down vehicular pollution. However, surveys depict limited effect of this policy3. Recently, Bangalore decided to install purifiers to improve air quality which according to researchers is unlikely to be useful since it is focused on effects rather than cutting down the sources4!
“If you can’t measure it, you can’t improve it!” – Kelvin.
On similar lines, in order to understand and solve any problem better, the most crucial step is to correctly identify and quantify its sources. Currently, India has around 100 monitoring stations operated by Central Pollution Control Board (CPCB), giving an estimate of the air quality in a defined area, with a majority installed around the Delhi-NCR region. Other parts of the country have very sparse number of such sensors and researchers estimate that India needs about 4000 of them5.
Prof. Nipun Batra, Assistant Professor (Computer Science and Engineering) at IIT Gandhinagar (India’s first Green Campus), is currently utilizing machine learning (helps systems to automatically learn from experience) coupled with data fusion (integrates multiple sources to produce more consistent information) and sensor networks to create a fine-grained map of air quality and predict the appropriate locations (hotspots) for installing these sensors in different parts of India.
Air quality information is mainly monitored using Government installed regulatory grade sensors, satellite retrievals and low cost sensors. The expensive Government sensors provide efficient temporal coverage, whereas single read/ day satellites are good with spatial coverage. Although the low cost sensors have fine spatial and temporal coverage, they are not as accurate. India is extremely diverse in terms of landscapes, regions and lifestyles which makes source apportionment (estimation and quantification of sources contributing to air pollution) challenging. Prof. Batra is in the process of combining the pros of all three types of sensors to come up with a single device that is accurate, economical and easily installable in any region of the country!
Some common sources of air pollution include household emissions, motor vehicles, industrial facilities, forest and crop burning. A one-size-fits-all policy might not work in a country as large as ours. Thus, actionable and location specific data driven policy needs to be implemented. Prof. Batra and his group use the campus deployment data for studying how air pollution affects the construction workers at IITGN and are in the process of developing some out-of-the-box strategies to curb it.
While researchers and policy makers work towards creating effective schemes,
what can a citizen do?
Tree plantation, use of CNG and bicycles, proper waste segregation, plastic ban, vehicle pooling. Instead of pointing a finger at others, all of us should pledge to control air pollution by every means possible and it starts with accurate identification of sources followed by targeted actions. Otherwise, the year 2050 will witness the greatest mortality rates in south Asia with a significant impact on child health by the 2030s as predicted by WHO6!