Predicting PM2.5 based on micro-satellite imagery and low-cost sensor network using CNN-RT-RF Joint Model

Presently, 17 out of 30 Indian cities are ranked worst in air quality around the globe due to high emissions of fine particulate matter, PM_2.5 (particles less than 2.5 µm diameter). These particles can reach deeper into the lungs and cause serious health problems, including cardiovascular obstructive pulmonary disease, lung cancer, stroke, and asthma. To take prompt actions towards mitigating and controlling the adverse effects of air pollution, it is important to monitor the ambient air quality regularly and at the neighbourhood level. However, the distribution of the regulatory central ambient air quality monitoring stations (CAAQMS) in India is sparse, and many states and cities lack any regulatory stationary monitors (RSMs). Conventional air quality monitoring techniques are inefficient and incapable of mapping PM_2.5 at a sub-Km level. The heterogeneity of PM_2.5 concentrations at large-scale and high spatial resolution has numerous applications in epidemiological studies, detecting hotspots within neighbourhoods and implementing policy interventions at local, regional and city levels. Therefore, an integrated monitoring framework is needed to fill gaps in the existing air quality measurements. This study proposes a tribrid approach of using the low-cost sensor (LCS) network to supplement the RSMs in generating more ground-truth PM_2.5 concentrations along with high-resolution micro-satellite imageries (PlanetScope, ~3m/pixel) to estimate and generate the PM_2.5 concentration maps at the sub-Km level (~500m by 500m). In the present study, an extensive LCS network of 70 nodes deployed at optimally selected locations within and around the boundaries of Lucknow city, Uttar Pradesh, India, along with six existing RSMs for one year (December 2021 onwards). It has increased monitoring ten folds at a moderate cost, covering remote urban and rural areas. The locations of these LCS and RSMs (76 nodes) have been used to precisely extract the daily (every day Dec 2021-2022) high-resolution satellite imageries by forming the area of interest (AOI) of size 224 by 224-pixel around the node while keeping the node in the middle of AOI. These imageries have been labelled with the ground truth PM_2.5  values from the nodes with geographical location and meteorological parameters such as relative humidity, atmospheric temperature, and barometric pressure. These labelled data are then fed into a deep learning CNN-RT-RF (Convolutional neural network- random trees-random forest) joint model to predict PM_2.5 at sub-Km level, which provides RMSE~ 2.74 and 7.50 for training and test data, respectively. The study further compares model performance with existing datasets of Delhi and Beijing. The results show that the predicted PM_2.5 using satellite imagery shows a strong co-relation with LCS and RSMs network and thus can be used as a soft sensor for large-scale monitoring. This study is the first study to integrate LCS sensor data with microsatellite imagery, leveraging over costly, conventional methods using machine learning approaches.