Abstract:
According to World Health Organization (WHO), the air in Lahore, Pakistan, has an
annual average of 68 µg/m3 of PM 2.5 particles which are 6.8 times more than safe
levels recommended by WHO. In December 2016, the amount of PM2.5 went above
100 µg/m3 which was more than 10 times safe levels set by WHO. Currently, there is
no network for real time air pollution prediction available in Lahore, nor Pakistan.
In this research, an Internet of Things (IoT) based low cost/low power outdoor air
quality monitoring system has been developed. We developed the wireless sensor nodes
for the monitoring and prediction of the PM2.5 particles in the air. The nodes comprised
of different sensors for measuring PM2.5, SO2, O3, CO, NH3, NO2, humidity, and
temperature values and Long Range (LoRa) transceiver for uploading the data on the
cloud. The coverage range of LoRa gateway has been tested in Lahore, Pakistan and it
is found that coverage range of the sensor node is around 2.7 KM in densely populated
areas. Five different artificial neural network models are used for the prediction ahead
of 1hr, 2hr, 3hr, 4hr, 5hr and 6hr. We obtained the accuracy of 99% for tested model of
LSTM. The accuracy of LSTM model for 1hr ahead is 13% more than FFNN, 2% than
Elman NN, 0.41% than NARX and 0.45% than layer recurrent. The accuracy of LSTM
model to predict 6hr ahead is above 95%. For 6hr ahead prediction, accuracy of LSTM
is 15% better than FFN, 12% than Elam NN, 7% than layer recurrent and 9% than
NARX NN. Results show that the LSTM model outclass all other models. The
computational time of LSTM model to predict 6hr ahead is 150.22565msec. While FFN
takes 30 msec, Elman NN takes 60msec, NARX takes 80msec and layer recurrent take
90 msec. The 1hr ahead predicted model of feed-forward NN embedded in all nodes
and placed at different places for real-time testing. The dataset collected at the serverside analyzed and it is found that our air pollution monitoring system is capable of
accurately predicting the future (1 hour ahead) concentration of PM 2.5 in outdoor air.
Its accuracy for 1hr ahead prediction is up-to 90% and its algorithm takes 0.642 micro
seconds computational time for the prediction level of PM2.5.
