Real-time Source Apportionment Research Articles

A novel calibration method for continuous airborne metal measurements: Implications for aerosol source apportionment

Continuous metal monitors have been widely used in environmental monitoring due to the high temporal resolution, high detection limit, and necessity for near real-time source apportionment. However, the reliability of the conventional calibration method, the deviation caused by uncalibrated monitoring data, and the subsequent impact on source identification results are rarely discussed. In this study, a reliable multi-point calibration approach by Primary Standard Aerosol Mass Concentration Calibration System (PAMAS) for the Xact625i Ambient Metals Monitor was developed and applied. The measured data was almost meaningless in the low-concentration range with bias even exceeding 100 % by using the conventional single-point calibration method based on thin-film standards. PAMAS was utilized to generate aerosols with known concentrations of the 20 metal elements including Al, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Sr, Cd, Sn, Sb, Ba, Tl, Pb, and Bi, in two concentration ranges of 150–1200 ng m−3 and 2.5–30 ng m−3 to validate the Xact625i Monitor. The results showed that the elemental concentrations were underestimated, especially in the low-concentration range, only for Cr, As, and Sr with slopes close to unity (1.00 ± 0.03). After calibration by PAMAS, the slopes of the linear relationships between measured and standard concentrations were all unity for the 19 elements in the high-concentration range, and close to unity for the 15 elements in the low-concentration range, and the accuracy of the remaining elements was also improved. After considering the calibration of aerosol metal data, it was found the number of source factors and their contributions to metals and PM2.5 in Chongming Dongtan, China, based on the PMF model significantly changed. This study highlighted the need of developing reliable calibration methods for online aerosol monitoring instruments and implied that the source apportionment results could be biased without careful data calibration.

Machine Learning Classification Model to Label Sources Derived from Factor Analysis Receptor Models for Source Apportionment

Factor analysis (FA) receptor models are widely used for source apportionment (SA) due to their ability to extract the source contribution and profile from the data. However, there is subjectivity in the source identification and labelling due to manual interpretation, which is time-consuming. This raises a barrier to the development of the real-time SA process. In this study, a machine learning (ML) classification algorithm, k-nearest neighbour (kNN), is applied to the source profiles obtained from the United States Environmental Protection Agency’s (US EPA) SPECIATE database to develop a model that can automatically label the factors derived from FA receptor models. The train and test score of the model is 0.85 and 0.79, respectively. The overall weighted average precision, recall and F1 score is 0.79. The performance of the model during validation exhibits acceptable results. The application of ML models for source profile labelling will reduce the time taken and the subjectivity associated with results due to modeler bias. This process can act as another layer of the process for verification of the results of FA receptor models. The application of this methodology advances the process towards real-time SA.

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. PM2.5 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 PM2.5 levels. The mean PM2.5 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 PM2.5 levels originated from local/regional combustion sources located east and south-west of the urban site, especially when high PM2.5 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 PM2.5, real-time source apportionment, and health risk estimates to benefit both policy makers, and the general public.