Z-number-based AQI in rough set theoretic framework for interpretation of air quality for different thresholds of PM2.5 and PM10

Dutta, Debashree; Pal, Sankar K.

doi:10.1007/s10661-022-10325-z

Cited by 3 publications

(2 citation statements)

References 77 publications

(89 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is triggered by human activities that disrupt environmental balance. Heavy metals are critical components of air pollution because they can be poisonous and deadly even at low concentration, and even necessary elements for organisms can be harmful at high concentrations (Dutta & Pal, 2022b ; Jo et al, 2020 ). Sevik et al ( 2020 ) determined the variation of Pb and Mg accumulation depending on plant species, plant organ, washing status, and traffic density in some landscape plants grown in the city center of Kastamonu.…”

Section: Introductionmentioning

confidence: 99%

“…Pollutant and particulate concentrations are predicted using support vector regression (SVR) models (Lei et al, 2022 ; Liu et al, 2017 ). Dutta and Pal ( 2022b ) have recently used a rough set theory-based decision support system to predict the different categories of AQI and developed a Z-number-based novel quantification measure of semantic information of AQI to assess the reliability of the outcomes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction and assessment of the impact of COVID-19 lockdown on air quality over Kolkata: a deep transfer learning approach

Dutta

Pal

2022

Environ Monit Assess

Self Cite

View full text Add to dashboard Cite

The present study focuses on the prediction and assessment of the impact of lockdown because of coronavirus pandemic on the air quality during three different phases, viz., normal periods (1 January 2018–23 March 2020), complete lockdown (24 March 2020–31 May 2020), and partial lockdown (1 June 2020–30 September 2020). We identify the most important air pollutants influencing the air quality of Kolkata during three different periods using Random Forest, a tree-based machine learning (ML) algorithm. It is found that the ambient air quality of Kolkata is mainly affected with the aid of particulate matter or PM (PM 10 and PM 2.5 ). However, the effect of the lockdown is most prominent on PM 2.5 which spreads in the air of Kolkata due to diesel-driven vehicles, domestic and commercial combustion activities, road dust, and open burning. To predict urban PM 2.5 and PM 10 concentrations 24 h in advance, we use a deep learning (DL) model, namely, stacked-bidirectional long short-term memory (stacked-BDLSTM). The model is trained during the normal periods, and it shows the superiority over some supervised ML models, like support vector machine, K-nearest neighbor classifier, multilayer perceptron, long short-term memory, and statistical time series forecasting model autoregressive integrated moving average. This pre-trained stacked-BDLSTM is applied to predict the concentrations of PM 2.5 and PM 10 during the pandemic situation of two cases, viz., complete lockdown and partial lockdown using a deep model-based transfer learning (TL) approach (TLS-BDLSTM). Transfer learning aims to utilize the information gained from one problem to improve the predictive performance of a learning model for a different but related problem. Our work helps to demonstrate how TL is useful when there is a scarcity of data during the COVID-19 pandemic regarding the drastic change in concentration of pollutants. The results reveal the best prediction performance of TLS-BDLSTM with a lead time of 24 h as compared to some well-known traditional ML and statistical models and the pre-trained stacked-BDLSTM. The prediction is then validated using the real-time data obtained during the complete lockdown due to COVID second wave (16 May–15 June 2021) with different time steps, e.g., 24 h, 48 h, 72 h, and 96–120 h. TLS-BDLSTM involving transfer learning is seen to outperform the said comparing methods in modeling the long-term temporal dependency of multivariate time series data and boost the forecast efficiency not only in single step, but also in multiple steps. The proposed methodologies are effective, consistent, and can be used by operational organizations to utilize in monitoring and management of air quality.

show abstract

Section: Introductionmentioning

confidence: 99%