Air Pollution Analytics - IoT

India is a developing nation. One of the necessities of a developing nation is well-connected markets, both electronically and physically. With the population growth exploding at an unprecedented rate, we are facing huge problems with respect to food security, medical care, transportation and ecosystem sustenance.

One of the major problems we are currently facing in the urban facade of India is Air Pollution. Contrary to the popular beliefs, air pollution is not just caused by transportation systems alone. There can be so many sources for air pollution in a city, such as Industrial Exhaust, Fossil fuel based power generation systems, Construction, Inefficient garbage disposal, among several other factors. Hence, it's difficult to keep track of all these sources temporally, geographically and politically so as to measure the impact and undertake corrective steps.

In order to make the right decisions, it becomes extremely important to have access to relevant and meaningful data. With respect to handling air pollution, hence it becomes important to gather data on air quality from various geographical locations in near-real time. The following steps provide a bird's eye view on how Air Pollution Analytics are enabled by IoT and how they can transform the decision-making on handling Air pollution.

1. Measuring Air Quality - Air Quality must be meaningfully measured in accordance with the type of pollution we are trying to measure. For cities, we can consider air mostly comprises of the pollutants such as Carbon Dioxide, Carbon Monoxide, Nitrogen Dioxide, Nitrogen Monoxide, Ozone, Fine and particulate matter. All these components must be measured against a set acceptable limits and thus can be categorized as Good, Moderate, and Poor.
2. Spreading the Sensors geographically - Each air quality sensor can cover a particular geographical area with acceptable representation. Beyond this, the information collected may not be applicable. Hence it is important to understand this limitation and spread the sensors with appropriate density across the city.
3. Connecting the sensors to the network - This is where the term IoT comes to life. It is important that all these sensors are connected to a network (not the Internet) so as to ensure a live streaming of air quality data to a central server which monitors the air quality in real time. This will also enable the sensors to be void of any storage mechanisms which must be physically accessed.
4. Mapping the AQI (Air Quality Index) geographically - As discussed earlier, all the data that is received from the sensors must be meaningfully mapped to a set of categories that provide an overview of the air quality in an area. This way, an entire city must be mapped with thousands of sensors, and represent the city with a heat map of the level of pollution in real time.
5. Identifying the pollution sources - The real-time pollution heat map generated in the previous step helps us in understanding the level of pollution in the city real-time. This information can be used in understanding and identifying the cause of the pollution and take immediate actions to curb it. Otherwise, it sometimes may be extremely difficult to gauge the amount of pollution caused by indiscriminately burning the garbage, until you measure it. This helps in understanding the heavily polluted areas in the city and taking steps to identify the sources and curb them.
6. Unlocking the secrets of patterns - It's extremely important to mine the data for meaningful insights and patterns. Patterns of pollution can help us in understanding the industry emission times, construction emission times, garbage disposal times and also in mapping the traffic patterns. These patterns can then be used to at least better schedule the activities and relocate some of them, if not eliminate all at once.
7. Policy Making - This data can also be used to make and enforce policies on air quality, to keep it under a certain healthy level. While many other policies are needed to achieve this, the data and insights can present the true dirty picture of the air quality and present us the real sense of urgency while giving us a tangible way to measure it real time.

There are two parts in solving a problem. Understanding it correctly is a major part. If we understand the problem as it is intended to be, rather than how it suits us. we have thus solved most of it. To understand we must posess the knowledge in the field of the problem. To attain the knowledge, we must have necessary information. And we need a system to provide us the information we need, so that we can solve the problem as best as we could.