Deployment of networked low-cost sensors and comparison to real-time stationary monitors in New Delhi

Abstract

Air quality is a global challenge issue, and many regions of the world, such as India, are experiencing daunting challenges. An important aspect is to identify and then control the emissions from major contributing sources. To advance this aspect, this paper describes an air quality network that has been set up in the National Capital Territory of Delhi (NCT-Delhi) to identify major contributing source categories in real-time. The various components include an innovative cloud-based dashboard to compile the data in real-time from a series of PM instruments (Beta Attenuation Monitors (BAM)) and a low-cost sensor network (22 APT- MAXIMA sensors). Furthermore, at one of the locations (urban site), three real-time chemical speciation monitors are installed to provide elemental speciation (30 elements), elemental carbon (EC), and organic carbon (OC) data. PM_2.5 concentrations at different sites (urban, industrial, and background) were compared to the BAM measurements over an 8-month period from May 2019 to February 2020; spanning the summer, monsoon, autumn, and winter seasons in Delhi. The APT sensor measurements were well correlated to the BAM measurements, with R² values ranging between 0.84 and 0.95 for all sites. This validated that the APT-MAXIMA low-cost sensors can be a useful tool for distributed monitoring of PM_2.5 levels. The mean PM_2.5 concentrations showed a trend with winter (Dec, Jan, Feb: 205.2 ± 95.1 µg m^-3) and autumn (Oct, Nov: 171.7 ± 128.3 µg m^-3) highs and summer (May, Jun: 64.6 ± 57.2 µg m^-3) and monsoon (Jul, Aug, Sep: 27.6 ± 16.7 µg m^-3) lows. The bivariate polar plot reveals high PM_2.5 levels originated from local/regional combustion sources located east and south-west of the urban site, especially when high PM_2.5 episodes are encountered during the festival season and other smog episodes.Implications: Low-cost sensors are useful for distributed monitoring under both low and high pollution conditions. A cloud-based dashboard system provided real-time, remote access to the data and in the visualization of air quality in the entire region. The real-time data availability on the cloud enabled establishing hot-spot regions of air pollution, spatial variation of PM_2.5, real-time source apportionment, and health risk estimates to benefit both policy makers, and the general public.