How Can Analytics Help Indian Cities Breathe Easy?

How Can Analytics Help Indian Cities Breathe Easy?

By Dr. Radhakrishna B, Director – Customer Advisory, SAS

Air pollution has become one of the significant environmental and health risks and has resulted in premature deaths over the course of time. Many cities across India show alarming rates of air quality, especially during winter. While efforts to curb emissions have been made, the sheer scale and complexity of the problem demand innovative solutions—and technology could play a transformative role.

Predictive analytics and artificial intelligence (AI) offer a powerful means to address the multifaceted challenge of air pollution. While these tools may not be a silver bullet, they can significantly enhance our ability to monitor, forecast, and mitigate the effects of poor air quality. By leveraging the power of data, governments, urban planners, and policymakers can adopt a proactive approach to safeguarding public health and improving environmental outcomes.

Stubble Burning Detection: Leveraging Satellite Data and AI for Real-Time Monitoring

One of the major contributors to air pollution is stubble burning. Each year, large swathes of crop residue are set on fire, releasing harmful particle matter into the atmosphere and exacerbating poor air quality during certain seasons.
Advancements in satellite technology combined with AI have showcased the potential to revolutionize the monitoring process of these incidents. AI models can be trained to analyze real-time satellite data to detect active stubble burning incidents. These models can assess the intensity of the fires, measure the emissions being released, and predict the impact on air quality based on variables such as wind direction and the amount of crop waste burned. Additionally, the identification of these hotspots can aid in better air quality forecasting, allowing authorities to act swiftly and efficiently before pollution levels reach hazardous thresholds.

A long-term solution to the issue of stubble burning lies in providing farmers with alternative, eco-friendly methods of crop residue management. AI-powered advisory apps could offer localized, real-time recommendations to farmers in their native language, helping them explore alternative solutions such as bio-decomposers, machinery like balers, or even creating bioenergy from stubble.

These AI systems would analyze local conditions, such as crop types, residue quantity, and soil health, to suggest tailored solutions. By educating farmers on sustainable methods of disposal, these tools could significantly reduce the reliance on stubble burning, promoting environmental health while maintaining agricultural productivity.

Predicting the Unpredictable: Data-Driven Air Quality Monitoring
One of the biggest challenges in addressing air pollution lies in its variability. Air quality can fluctuate dramatically based on factors such as weather patterns, traffic density, and industrial activity. Traditionally, monitoring air quality is done by manual sampling and laboratory analysis which is often expensive, time-consuming, and error-prone due to multiple touchpoints. Predictive analytics—powered by advanced data models—can help forecast pollution levels with greater accuracy, allowing authorities to issue timely warnings and take preemptive measures.

For instance, AI-driven models can analyze vast datasets from satellite imagery, IoT-enabled air quality sensors, and historical pollution records to predict when and where air quality is likely to deteriorate. Armed with such insights, city administrators can implement measures, such as restricting vehicular movement or upgrading industrial control of emissions, and suspend activities in hotspots, to reduce pollution spikes.

As a global leader in data and analytics, SAS has witnessed how predictive technologies have been used to tackle complex challenges across the world, from natural disaster management to urban planning. For example, in 2019, Jakarta Smart City developed Jakarta Kini (JAKI), a digital platform and app that uses SAS® Analytics for IoT to predict and monitor areas at risk of flooding, close to real time. With predictive capabilities, Jakarta officials can better determine when to activate water pumps, accelerate disaster response, and improve preparedness for flood emergencies.

Similarly, the Istanbul Metropolitan Municipality (IMM) has harnessed SAS analytics to address the transportation challenges of a rapidly growing city. SAS-powered AI and machine learning solutions analyze historical and live traffic data to optimize bus routes and anticipate traffic anomalies, such as accidents and lane closures, in real time. By creating a smarter urban infrastructure, IMM has improved transportation efficiency and reduced congestion in Istanbul —showcasing how predictive analytics can transform city management. These examples highlight the transformative power of predictive analytics in addressing environmental challenges.

Strengthening Disaster Response

The impact of hazardous air quality is akin to that of a slow-moving disaster. Timely intervention is key to aid citizens. Here are some ways AI-powered disaster response tools can play a critical role in minimizing adverse effects:
Early Warning Systems for Proactive Planning
Analytics-driven air quality forecasting would provide authorities with critical lead time, predicting hazardous conditions a few days in advance. This would allow city administrators to implement timely measures such as school closures, restrictions on heavy vehicles, or increased public awareness campaigns. Such foresight not only mitigates immediate harm but also minimizes disruptions by enabling better preparedness.

Optimized Traffic and Urban Management
AI models can play a pivotal role in reducing pollution caused by vehicular traffic. These models can recommend creating vehicle-free zones, implementing staggered work hours, or even introducing dynamic traffic flow adjustments based on real-time air quality data. For instance, vehicle restrictions in identified hotspots can lead to a measurable reduction in emissions, while incentivizing the use of public transport or cycling could contribute to long-term sustainability.

Targeted Interventions and Hotspot Analysis
Analytics tools are adept at pinpointing pollution hotspots within a city, which could enable highly localized interventions. For example, insights from these models can lead to temporary construction bans, stricter industrial emission controls, or the installation of portable air purifiers in affected areas. By concentrating resources where they are most needed, cities can address pollution more effectively and efficiently.

Policy Impact Assessment and Continuous Improvement
Policy effectiveness can also be evaluated with the help of continuous data monitoring. By comparing pollution levels before and after implementing measures such as odd-even vehicle schemes or increased green cover, authorities can determine which strategies yield the best outcomes. Such iterative assessments ensure that policies evolve based on evidence, leading to smarter, more impactful governance.

Healthcare Preparedness and Predictions
Predictive models linking air pollution to public health outcomes can help healthcare systems prepare for surges in respiratory and cardiovascular illnesses and could even help identify vulnerable populations—such as children, the elderly, and those with preexisting conditions—and prioritize them for protective measures. Hospitals and clinics can use these insights to optimize resource allocation, ensuring adequate availability of medical supplies and staffing during peak pollution periods. For example, forecasting pollution-induced health crises enables authorities to establish temporary healthcare units or deploy mobile health clinics to underserved areas.

Beyond immediate mitigation, predictive analytics and AI can also help shape long-term strategies to combat air pollution. Risk assessment models can identify the root causes of pollution, enabling policymakers to design targeted interventions that address specific sources of emissions. For example, AI could analyze traffic patterns to recommend adjustments in urban planning, such as creating more efficient public transport systems, redesigning road networks to reduce congestion or incorporate green infrastructure to act as natural air filters.

The Road Ahead
The potential of AI and predictive analytics to address the air quality crisis is immense, but unlocking this potential requires collaboration across sectors. Governments, technology providers, academia, and civil society must come together to build and deploy these solutions effectively. The technology’s success hinges on robust data-sharing frameworks, investments in infrastructure, and a commitment to ethical AI practices that prioritize public welfare.

By embracing a data-driven approach, we can move from reactive measures to proactive strategies, paving the way for cleaner skies and healthier communities. India’s air quality crisis is a reminder that time is of the essence. Leveraging the power of predictive analytics and AI could mark a turning point in how we approach environmental challenges, offering hope for a future where citizens can finally breathe easy.

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