It is now a well-established fact that exposure to poor air quality leads to serious health conditions. This impact can be minimized by reducing our exposure to air pollution. We can do this by leveraging the latest technology which is available to us. One of the best ways of doing that is forecasting air quality in our cities. With the availability of sophisticated data analysis techniques and access to required computational resources, we have all the components necessary for forecasting air quality. All we need is good quality input data to generate accurate forecasts.

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HOW AIR QUALITY
FORECASTING CAN
HELP US REDUCE
POLLUTION EXPOSURE?

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It is now a well-established fact that exposure to poor air
quality leads to serious health conditions. These health
impacts on populations have led to a massive economic
burden on economies around the world.
This impact can be minimized by reducing our exposure
to air pollution. We can do this by leveraging the latest
technology which is available to us. One of the best ways
of doing that is forecasting air quality in our cities.
INTRODUCTION AIR QUALITY FORECASTING

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AIR QUALITY FORECASTING
Air quality forecasts are generated by various air
quality models.
An air quality model is a collection of a number
of mathematical equations. These equations
simulate various physical and chemical
atmospheric processes.
If the model of a forecast system is an engine,
the input data is fuel for that engine.
The model and input data together are the
building blocks of a good forecasting system.
BUILDING BLOCKS OF AIR QUALITY FORECAST

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An air quality forecasting system essentially requires
two datasets. First, the condition of current air pollution
and second, meteorological data.
Large numbers of input data points are required to
improve the quality of the model's output.
Conventional air quality monitors due to their high cost
cannot be deployed on such a large scale.
The integration-friendly design of Oizom devices
provides smooth and seamless real-time data
monitoring capability which is perfect for an air quality
forecasting system.
INPUT DATA FOR FORECAST SYSTEM AIR QUALITY FORECASTING

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MORE DATA, MERRIER FORECASTS
With modern data assimilation capabilities,
models have evolved to be much more capable
of providing highly accurate forecasts. Additional
data layers can be added for insightful air quality
forecasts.
City officials, researchers, and policymakers can
use the forecast results with other datasets to
make data-driven decisions.
For instance, forecast results coupled with
demographics data and epidemiological
datasets can aid the formulation of mitigation
strategies for pollution-sensitive areas.
AIR QUALITY FORECASTING

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The goal of any technology is to help and improve
the lives of people. It is also the central goal of smart
cities to provide a clean and healthy environment
for its citizens.
Air quality forecasts can help city administration in
issuing health alerts and advisory. It can be
especially beneficial to sensitive populations. People
can employ various pollution reduction measures to
reduce their exposure to pollution.
REDUCING EXPOSURE WITH FORECASTS AIR QUALITY FORECASTING