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How environmental analytics helps combat pollution

environmental analytics

While everyone is shut inside because of the pandemic, it is important to assess our relationship with the environment. You would have also heard about how China’s carbon emissions have fallen by 100 million metric tonnes, leading to much cleaner air.

What this shows us is that we have a precarious relationship with our environment.

While it’s important to stay safe, as this pandemic spreads, it’s important to take into account our relationship with the environment, as well. Carbon emissions may be down, but if we don’t change our processes, then a return to the status quo is inevitable.

Hence, why we are looking at environmental analytics and how it’s the best solution for combatting the debilitating effects of pollution.

What can analytics do to save the environment?

One of the biggest benefits of environmental analytics is its versatility. The analytics solution can be used to address multiple issues ranging from air pollution to oil dumping. It’s impossible to cover them all in a single blog, so we are going to focus on a few.

Identify the most serious areas

To combat pollution effectively, policymakers need to prioritise resources. This means dedicating resources to the most serious causes. However, that can be impossible to do through conventional means. Environmental analytics makes it possible to determine the biggest sources of pollution because it processes millions of variables from several data points.

This allows data analysts to pinpoint and identify the main sources of pollution from different regions of the world. For example, 100 South American cities are responsible for 20% of global pollution.

These findings would not be possible without environmental data analytics. With quantifiable evidence to back these findings, policymakers are more inclined to invest in the necessary resources to hit back against the environment.

Predict environmental disasters

Environmental analytics can help organisations anticipate climate change trends before they even happen. While everyone agrees that environmental conditions are getting worse, it’s difficult to predict their precise effects until they happen. However, predictive data models are possible thanks to analytics software.

Organisations can use environmental analytics to predict how the weather changes over time. For example, data analytics can assimilate data on CO2 and even predict the amount of CO2 in the atmosphere in the next few years. This can be used to adjust policies to reduce CO2 output.

Real-world applications of environmental analytics

We are already seeing environmental analytics at play in different countries like Beijing and Brazil. We discover the true worth of these platform in these real-life applications.

In 2015, the Beijing Environmental Bureau (EPB) collaborated with IBM’s Research Lab to better understand pollution contributors. Using environmental analytics, the IBM Research Lab developed one of the world’s most advanced air quality forecasting and decision support systems.

These systems generated 1-by-1 km pollution forecasts 72 hours ahead and even predicted pollution trends up to 10 days into the future. This allowed IBM to model and predict the effects of weather on airflow and the spread of airborne chemicals from pollutants. This insight allowed the Beijing government to achieve a 20% reduction in ultra-fine Particulate Matter in the air.

Telefonica Brazil was an initiative to use big data to monitor air pollution in Sao Paulo and improve the city’s traffic management/environmental planning. The goal of the project was to understand the connection between population movements and its effect on overall air quality.

Such an initiative was possible only because of the use of big data to predict pollution problems. By collecting and analysing data on people’s movement, Sao Paulo authorities were able to predict pollution problems two days ahead. This allowed authorities to take extra precautions to improve public health. For example, redirecting traffic from areas where air quality is low.

This initiative was possible because algorithms from environmental analytics calculated movement and traffic trends and the algorithms then used machine learning models to map out accurate forecasts.

Resolving environmental issues with analytics

It was the relentless pursuit of technological progress that compromised the environment and, ironically, it is also technology that will help us reduce the damage inflicted on nature.

While the Coronavirus has ground most of the world to a halt, it’s important to take this moment to assess our relationship with nature and fix it using technology, specifically with environmental analytics.