  The release of toxic gases by industries, emissions from vehicles, and an
increase in the concentration of harmful gases and particulate matter in the
atmosphere are all contributing factors to the deterioration of the quality of
the air. Factors such as industries, urbanization, population growth, and the
increased use of vehicles contribute to the rapid increase in pollution levels,
which can adversely impact human health. This paper presents a model for
forecasting the air quality index in Nigeria using the Bi-directional LSTM
model. The air pollution data was downloaded from an online database (UCL). The
dataset was pre-processed using both pandas tools in python. The pre-processed
result was used as input features in training a Bi-LSTM model in making future
forecasts of the values of the particulate matter Pm2.5, and Pm10. The Bi-LSTM
model was evaluated using some evaluation parameters such as mean square error,
mean absolute error, absolute mean square, and R^2 square. The result of the
Bi-LSTM shows a mean square error of 52.99%, relative mean square error of
7.28%, mean absolute error of 3.4%, and R^2 square of 97%. The model. This
shows that the model follows a seamless trend in forecasting the air quality in
Port Harcourt, Nigeria.
