Embracing Data-driven Solutions To Combat Air Pollution

Air, the very element that sustains life, is shared by all, transcending socio-economic barriers and boundaries. Yet it is not treated with the equity it deserves. We may live in different parts of a city or country, but air pollution affects each of us in varying degrees, regardless of our social status. This challenge is particularly evident in cities like Delhi which often gets featured in the world’s most polluted list, with the air quality index frequently crossing acceptable limits. The result is a public health crisis that touches millions each day. A stark reminder of this came during the recent Delhi Half Marathon, where several participants experienced shortness of breath, highlighting how routine physical activities become challenges due to poor air quality. 

The Public Health Crisis
Air pollution is a leading cause of premature deaths globally. According to the World Health Organization (WHO), exposure to contaminated air causes over 7 million premature deaths annually, with low- and middle-income nations bearing the brunt of this burden. Airborne and cardiovascular illnesses, cancer, and impaired mental health outcomes are all caused by pollutants such as particulate matter (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ground-level ozone. Data from India reflects this grim reality: As per https://www.bmj.com/content/383/bmj-2023-077784 air pollution causes over 2 million deaths in India every year . Chronic respiratory and cardiovascular disorders are becoming more prevalent, resulting in billions of dollars in costs for the healthcare system.

Counting the Hidden Costs
Air pollution doesn't just impact health, it also has economic consequences.  A 2021 analysis by Greenpeace and the Centre for Research on Energy and Clean Air (CREA) estimates that air pollution costs India about $36.8 billion annually, or 1.36 per cent of its GDP.  The reasons are sick days taken by workers, increased healthcare costs, and decreased agricultural productivity. According to a study that was published in the journal Environmental Research, even modest drops in air pollution can have a significant positive economic impact. For example, lowering New Delhi's yearly PM2.5 level by 10 µg/m³ might save millions of dollars in medical expenses and boost worker productivity by 7 per cent.

Current Responses and Measures
India has made notable strides in addressing air pollution, led by initiatives like the Commission for Air Quality Management (CAQM), the Graded Response Action Plan (GRAP), and the Central Pollution Control Board's (CPCB) efforts. These frameworks aim to reduce pollution through structured responses, especially in critical regions such as the National Capital Region. For example, during extreme air quality instances, GRAP restricts industrial activity and outlaws diesel generators, enforcing progressive steps based on pollution levels. 

India’s National Clean Air Programme, launched in 2019, targets a 20-30% reduction in particulate matter in 131 cities by 2024, implements stricter emission norms,  promotes electric vehicles, and guides interventions through a network of 1,400 monitoring stations for real-time data. 

Additionally, emergency measures are used during periods of extreme pollution, such as bans and fines to reduce the pollution generated by burning crop stubble before new planting seasons and Delhi's odd-even vehicle system, which temporarily limits the number of cars on the road. These actions, however, frequently have a brief lifespan and lack the steady implementation required for lasting effects. Without comprehensive, data-driven solutions, these measures act as band-aids rather than the holistic intervention needed to address the issue.

Data-Driven Policies for a Cleaner Future
Addressing air pollution requires moving from short-term fixes to data-driven, sustainable policies. By embracing data-driven policies, we can better understand the sources, causes, and effects of pollution and tailor interventions accordingly. 

The deployment of sensor networks in Delhi demonstrates how real-time monitoring and predictive analytics enable authorities to pinpoint pollution hotspots and take action before the quality of the air deteriorates. Preventive actions, such as reducing the burning of crop residue prior to seasons of high pollution, are made possible by predictive models' ability to predict high-risk times.

Localized data is crucial as well, as it allows cities to impose specific controls, such as emissions caps and traffic limits, tailored to pollution-heavy areas. Some cities in Europe, like Paris and Amsterdam, have successfully adopted "Low Emission Zones" based on this principle, which restrict access for high-emission vehicles.

Governments can evaluate policy efficacy and make necessary adjustments by combining environmental indicators with public health data. Apps like the "Sameer," created by the Central Pollution Control Board of India, enable users to get health advisories and track air quality levels in real time. Data from these apps can provide government important information about patterns in community health, enabling them to focus resources and efforts where they are most needed.

Combating air pollution is a complex but achievable goal. Data-driven policies can provide the foundation for change, where traditional approaches often lacked the accuracy and flexibility needed for sustainable results, advances in AI such as Artificial Neural Networks (ANN) can now enable real-time air quality monitoring, predictive modeling, and data integration from sources such as traffic and pollutant concentration systems. Integrating AI with big data is  also transforming air quality management, empowering policymakers to act proactively and sustainably for healthier cities. By utilising data and technology while designing and implementing policies, India can make significant strides toward achieving cleaner air and improving public health.