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Analysis and prediction on real time air quality data is a critical step in solving various problems related to pollution and finding a genuine solution. However, missing values in air pollution data is a serious issue that may greatly influence the performance of such analysis and prediction. In order to address this problem, a 2-step process is proposed, consisting of a data pre-processing stage using Q-FUZZY (Quantized Fuzzification) model and data imputation stage via a Fuzzy Imputation Model(FIM) to handle this complexity. We have validated the proposed approach using a real world dataset of Kolkata, India containing pollution levels of different air pollutants along with meteorological parameters. Various performance measures are used to determine the effectiveness of FIM. Moreover, the performance of the proposed model is also compared with the baselines namely, FLAR (Fuzzy Least Absolute Regression), ridge regression and EMD-SVR-SARIMA (Empirical Mode Decomposition-Support Vector Regressions-Seasonal Autoregressive Integrated Moving Average). On comparison, it is observed that FIM achieves an overall better performance in terms of distance measures, Mean Similarity Measures (MSM), Mean Inclusion Measures (MIM) and Mean Predictive Ability (MPA). [ABSTRACT FROM AUTHOR]

Existence of several challenges and high cost in the development of monitoring infrastructure have become major reasons for data sparsity by statutory government agencies tasked to study pollution exposure in urban areas. As an effort to mitigate this problem, the recent usage of satellite aerosol optical depth data along with the usage of learning algorithms have become popular in recent times. This paper presents a novel four-staged approach using different machine learning, deep learning and statistical methods to develop a spatio-temporal hybrid model for temporal forecasting using data from existing stations along with satellite aerosol optical depth data for spatial interpolation. Experiments conducted on real-world data belonging to the cities of Kolkata, Bengaluru and Mumbai show that a consistent pattern is not followed in all the cities in all stages except in spatial interpolation where Random Forest Regression is found to surpass all other models used. While a long short-term memory network (LSTM Auto-Encoder) when employed in temporal forecasting inside the hybrid method outperforms others in Mumbai, a random forest regression-based method and a multi-layer perceptron-based method outperform others similarly in Kolkata and Bengaluru, respectively. [ABSTRACT FROM AUTHOR]

Tackling air pollution has become of utmost importance since the last few decades. Different statistical as well as deep learning methods have been proposed till now, but seldom those have been used to forecast future long-term pollution trends. Forecasting long-term pollution trends into the future is highly important for government bodies around the globe as they help in the framing of efficient environmental policies. This paper presents a comparative study of various statistical and deep learning methods to forecast long-term pollution trends for the two most important categories of particulate matter (PM) which are PM2.5 and PM10. The study is based on Kolkata, a major city on the eastern side of India. The historical pollution data collected from government set-up monitoring stations in Kolkata are used to analyse the underlying patterns with the help of various time-series analysis techniques, which is then used to produce a forecast for the next two years using different statistical and deep learning methods. The findings reflect that statistical methods such as auto-regressive (AR), seasonal auto-regressive integrated moving average (SARIMA) and Holt–Winters outperform deep learning methods such as stacked, bi-directional, auto-encoder and convolution long short-term memory networks based on the limited data available. [ABSTRACT FROM AUTHOR]

Poor air quality is becoming a critical environmental concern in different countries over the last several years. Most of the air pollutants have serious consequences on human health and wellbeing. In this context, efficient forecasting of air pollutants is currently crucial to predict future events with a view to taking corrective actions and framing effective environmental policies. Although deep learning (DL) as well as statistical forecasting models are investigated in the literature, they have rarely used in air pollutant-specific optimal model building for long-term forecasting. In this paper, our aim is to develop the pollutant-specific optimal forecasting models for the phases spanning from preprocessing to model building by investigating a set of predictive techniques. In this regard, this paper presents a methodology for long-term forecasting of some important air pollutants. More specifically, a total of eight best performing models such as stacked LSTM, LSTM auto-encoder, Bi-LSTM, convLSTM, Holt-Winters, auto-regressive (AR), SARIMA, and Prophet are investigated for developing pollutant-specific optimal forecasting models. The study is carried out based on the real-world data obtained from government-run air quality monitoring units in Kolkata over a period of 4 years. The models such as Holt-Winters, Bi-LSTM, and ConvLSTM achieve high forecasting accuracy with respect to MAE and RMSE values for majority of the pollutants. [Display omitted] • Long-term forecasting of six air pollutants namely PM2.5, PM10, SO2, NO2, NO, and O3. • Data modelling by investigating a set of statistical and deep learning methods. • Finding optimal forecasting models for different pollutants. • Time series analysis for various air pollutants in order to study underlying trends. • Evaluation of the performance of the models for long term forecasting. [ABSTRACT FROM AUTHOR]