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. 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.
The accuracy of a forecast is as good as the quality of underlying input data. High data quality and versatile applicability of devices is the central design objective of Oizom. We offer a range of air quality monitoring devices that can serve as a perfect data provider for a forecasting system.
THE BUILDING BLOCKS OF AIR QUALITY FORECASTS
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. They are supported by a framework of logical steps designed to provide accurate forecasts. These steps are governed by various algorithms specifically developed for forecasting.
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.
INPUT DATA FOR A FORECAST SYSTEM
An air quality forecasting system essentially requires two datasets, on which the variability of air quality depends. These two datasets are current air pollutant concentration and meteorological parameters.
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. On the other hand, sensor-based monitors such as Oizom’s Polludrone can be easily deployed to create a dense monitoring network.
One of the best features of Polludrone is that it can be easily equipped with additional sensors to record meteorological parameters. Oizom also offers a dedicated meteorological device, Weathercom for specific applications.
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.
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.
Real-time traffic data can help better simulate roadside pollution exposure. Air pollution data from satellites can serve as a great addition in understanding large-scale pollution transport to and from the city. Similarly, data from weather forecasting models can also be used to fine-tune 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.
REDUCING EXPOSURE WITH AIR QUALITY FORECASTS
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.
Assessing trends in the data, effective emission control programs can be developed to reduce the overall pollution load of the city. It can be further included in the city planning and regulations to design sustainable smart cities.
