Development of end-to-end low-cost IoT system for densely deployed PM monitoring network: an Indian case study

Particulate matter (PM) is considered the primary contributor to air pollution and has severe implications for general health. PM concentration has high spatial variability and thus needs to be monitored locally. Traditional PM monitoring setups are bulky, expensive, and cannot be scaled for dense deployments. This paper argues for a densely deployed network of IoT-enabled PM monitoring devices using low-cost sensors, specifically focusing on PM10 and PM2.5, the most health-impacting particulates. In this work, 49 devices were deployed in a region of the Indian metropolitan city of Hyderabad, of which 43 devices were developed as part of this work, and six devices were taken off the shelf. The low-cost sensors were calibrated for seasonal variations using a precise reference sensor and were particularly adjusted to accurately measure PM10 and PM2.5 levels. A thorough analysis of data collected for 7 months has been presented to establish the need for dense deployment of PM monitoring devices. Different analyses such as mean, variance, spatial interpolation, and correlation have been employed to generate interesting insights about temporal and seasonal variations of PM10 and PM2.5. In addition, event-driven spatio-temporal analysis is done for PM2.5 and PM10 values to understand the impact of the bursting of firecrackers on the evening of the Diwali festival. A web-based dashboard is designed for real-time data visualization

The Application of Mobile Sensing to Detect CO and NO₂ Emission Spikes in Polluted Cities

Carbon monoxide (CO) and Nitrogen dioxide (NO2) are major air pollutants that have the potential to affect human health adversely. There is a lack of useful information regarding the spatial distribution and temporal variability of CO and NO2 emissions in major metropolitan areas. The primary goal of this research is to provide a geospatial data methodology for detecting emission spikes of CO and NO2 in polluted urban environments employing portable, low-cost sensors. We propose that ephemeral identification of harmful gas concentrations can be achieved using different IoT device types mounted on a mobile platform. We propose that persistent CO and NO2 emission spikes can be identified by driving through the city on different days. We applied this approach to Hyderabad, India, by fixing a mobile platform on a street car. We corrected the IoT device measurement errors by calibrating the sensing component data against a reference instrument co-located on the mobile platform. We identified that random forest regression was the most suitable technique to reduce the variability between the IoT devices due to heterogeneity in the mobile sensing datasets. The spatial variability of CO and NO2 harmful emission spikes at a resolution of 50 m were identified, but their intensity changes on a daily basis according to meteorological conditions. The temporal variability shows a weak correlation between CO and NO2 concentrations. The data from the CO and NO2 emission spikes at Points of Interest that disturb traffic flows clearly show the need for public education about when it is hazardous for persons with respiratory conditions to be outside, as well as when it is unsafe for young children and the elderly to be outside for extended periods of time. This detection strategy is adaptable to any mobile platform used by individuals traveling by foot, bicycle, drone, or robot in any metropolis