During the past years, air quality has become an important global issue, due to its impact on people's lives and the environment, and has caused severe problems for humans. As a prevention to effectively control air pollution, forecasting models have been developed as a base for decision-makers and urban managers during the past decades. In general, these methods can be divided into three classes: statistical methods, machine learning methods and hybrid methods. This study's primary intent is to supply an overview of air pollution prediction techniques in urban areas and their advantages and disadvantages. A comparison has also been made between the methods in terms of error assessment and the use of geospatial information systems (GIS). In addition, several approaches were applied to actual data, and the findings were compared to those acquired from previous published literatures. The results showed that forecasting using machine learning and hybrid methods has provided better results. It has also been demonstrated that GIS can improve the results of the forecasting methods.

The present paper presents a list of probabilistic sampling procedures and subsequent statistical analysis, which may achieve this goal without a considerable increase of field effort

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.

Spatiotemporal variations in PM₂.₅ are a key factor affecting personal pollution exposure levels in urban areas. However, fixed-site monitoring stations are so sparsely distributed that they hardly capture the dynamic and fine-scale variations in PM₂.₅ in urban areas with complex geographical features and urban forms. Recently, a distributed air sensor network (DASN) was deployed in Dezhou city, China, to monitor fine-scale air pollution information and obtain deep insight into variations in PM₂.₅. Based on the data collected by the DASN, this paper investigated the spatiotemporal patterns of PM₂.₅ using the time-series clustering method. The results demonstrated that there were four stages of PM₂.₅ daily variations, i.e., accumulation, continuous pollution, dispersion, and cleaning. Generally, the stage of dispersion occurred more rapidly than the stage of accumulation, and PM₂.₅ accumulated easily in warm and humid weather with low wind speeds. However, the stage of dispersion was affected mainly by high wind speeds and precipitation. Additionally, the results suggested that four variation stages did not strictly correspond to seasonal divisions. The spatial distributions of PM₂.₅ revealed that the main pollution source was located in a southeastern industrial park, which exhibited a significant impact throughout the four stages. Considering both the temporal and spatial characteristics of PM₂.₅, this study successfully identified pollution hotspots and confirmed the effect of industrial parks. The study demonstrates that the DASN has high prospective applicability for assessing the fine-scale spatial distribution of PM₂.₅, and the time-series clustering method can also assist environmental researchers in further exploring the spatiotemporal characteristics of urban air pollution.