Venue: ESPC 2022 Competition

Reviews:

final_score: rating: 3: Accept. This work can be presented during the event, but it is not a contender to win the competition.
review_content: summary: The project presents AQI estimation based on IoT and ML. 
strengths: 1. The video and report are well presented.
2. The whole process (sensing, architecture, and data processing) is appropriate.

weaknesses: 1. Novelty is lacking. Machine learning methods are traditional and ideas of using traffic data and other environmental measurements for estimating AQI is not new.
2. The estimation is focus on AQI five levels instead of pollutant concentrations, which is not so promising from this point of view.
3. Some minor typos exist.
review_title: null

final_score: rating: 3: Accept. This work can be presented during the event, but it is not a contender to win the competition.
review_content: summary: The author has presented ML-based system to measure the AQI using traffic data. 
strengths: 1. Already prototype has been tested in the real environment
2. The system is well-designed and explained in proper manner
weaknesses: 1. How is it going to differentiate between an electric vehicle and a fuel vehicle?
review_title: null

final_score: rating: 3: Accept. This work can be presented during the event, but it is not a contender to win the competition.
review_content: summary: The authors proposal a ML based approach for estimating the Air quality Index using the traffic information, temperature, and humidity. The model is trained using PM values obtained from PM sensors.
strengths: - The problem of air quality monitor is important and becoming increasingly important, and researchers are trying to explore various cost effective solutions that scale. This makes the proposed solution timely. 
- The authors have collected ground truth information which is vital for training models. The system they use for collecting the ground trurh data has been deployed on actual roads.  The data from a mapping service provider (HERE maps) is used for traffic information
- The block diagrams and circuit architecture are detailed in the report and the presentation. 
- The overall sytem architecture is presented well in the video. 

weaknesses: - It is not very clear how the data for the NDVI score ($R_{nir} and A_{red}$) was obtained.  
- The key shortcoming is the black box ML approach, where the explainability was not considered.  Why is the accuracy and F1 score so low and what are the key factors that affect the accuracy and F1 score? It seems the authors are using a black box approach. For instance, how is the model predicting the AQI? How sensitive is it to each of the variables? What are the possible confounding factors that might be of interest?

Some points to ponder
- When will the road traffic data not be a good indicator for the AQI? For instance, consider a large number of electric buses, vs a large number of diesel trucks. What can be done to ensure that your system can distinguish between the two?
- It is not clear why the arithmetic mean is a good choice for the Jamming Factor. It would be nice if the authors can show the benefits/shortcomings of using the harmonic (or even geometric) mean when combining the Jamming factor.
review_title: null

final_score: rating: 3: Accept. This work can be presented during the event, but it is not a contender to win the competition.
review_content: summary: IoT and ML-based AQI Estimation using Real-time Traffic Data provides an interesting approach to monitoring atmospheric air quality. The team places greater emphasis on map based traffic mobility and NDVI than on IoT sensor measurements. The IoT sensors are used to complete the ML model that predicts Air Quality Index based on PM (particulate matter) classifications. This approach is very applicable to  cities with very or no sensor networks. However, there are many questions  on the reliability of the approach. Traffic emissions can be used as an emission inventory for an advanced air quality model but other emission inventories and meteorological data are still required (source apportioned) to make reasonable spatial and temporal forecasts.       



strengths: The problem is well known: Air pollution is a grave threat to human health and the environment. The IoT and ML-based AQI Estimation using Real-time Traffic Data project applies real-time traffic data monitor air quality. The team uses IoT devices to complement or validate the air quality data and applies ML to improve the data quality. 
 
This is a research project with potential applications. 

The prototype IoT PM monitoring device was developed and deployed in the main road and junctions in Hyderabad from Jan’22 - May’22. The PM measurements were compared to traffic data and NDVI measurements from the same period. For the environment challenge, the team applied  ML algorithms to predict the AQI from traffic and location data in real-time. The team Random Forest model on a rich traffic dataset with approximately 210,000 samples and the results appear to be good.

There  is potential to use traffic data as a proxy to generate an Air Quality Index. Particularly when the data stream is enriched by the IoT sensors, validating the PM concentration measurements.    
weaknesses: Much of this work was conducted before earlier this year and has been submitted for peer review publication.  

It is difficult to work out how the environmental monitoring system works. Figure 3. shows data flow from traffic map-based data, sensor data (temp and RH), and spectral NDVI measurements for vegetation greenery, and uses ML to compute levels of PM2.5 and PM10.  Which is divided into AQI categories and validated by the 15 IoT sensors. The first question is where is the PM coming from and how does the model assess the mass difference between fine (PM 2.5) and coarse (PM10) particles from the data sets? How is source apportioned managed by the datasets: traffic emission can account for 50% of city PM concentration;  the other 50% of city PM comes from residential, industrial, and from the other cities or agricultural burning sources. 

More information is need on the data fusion, and validating the actual PM atmospheric concentrations.
review_title: null

Decision: Accept