Air Quality Early Warning System Research Articles

Effectiveness of respiratory face masks in reducing acute PM2.5 pollution exposure during peak pollution period in Delhi

The cities of North India, such as Delhi, face a significant public health threat from severe air pollution. Between October 2021 and January 2022, 79 % of Delhi's daily average PM2.5 (Particulate matter with an aerodynamic diameter ≤ 2.5 μm) values exceeded 100 μg/m3 (the permissible level being 60 μg/m3 as per Indian standards). In response to this acute exposure, using Respiratory Face Masks (RFMs) is a cost-effective solution to reduce immediate health risks while policymakers develop long-term emission control plans. Our research focuses on the health and economic benefits of using RFMs to prevent acute exposure to PM2.5 pollution in Delhi for different age groups. Our findings indicate that, among the fifty chosen RFMs, M50 has greatest potential to prevent short-term excess mortality (908 in age ranges 5–44), followed by M49 (745) and M48 (568). These RFMs resulted in estimated economic benefits of 500.6 (46 %), 411.1 (37 %), and 313.4 (29 %) million Indian Rupee (INR), respectively during October–January 2021–22. By wearing RFMs such as M50, M49, and M48 during episodes of bad air quality, it is estimated that 13 % of short-term excess mortality and associated costs could be saved if at least 30 % of Delhi residents followed an alert issued by an operational Air Quality Early Warning System (AQEWS) developed by the Ministry of Earth Sciences. Our research suggests that RFMs can notably decrease health and economic burdens amid peak PM2.5 pollution in post-monsoon and winter seasons until long-term emission reduction strategies are adopted. It is suggested that an advisory may be crafted in collaboration with statutory bodies and should be disseminated to assist the vulnerable population in using RFMs during winter. The analysis presented in this research is purely science based and outcomes of study are in no way to be construed as endorsement of product.

Forecasting of an unusual dust event over western India by the Air Quality Early Warning System

This study presents the successful implementation of an operational Air Quality Early Warning System (AQEWS) in forecasting an unprecedented dust storm event that occurred in the western part of India from 21–24 January 2022. The AQEWS generates a daily 72-h forecast using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) at the core, which is initialized with assimilated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals and in-situ measurements of fine particulate matter (PM2.5). The AQEWS was able to forecast the outbreak of this dust storm reasonably well over the north of the Arabian Sea on 21 January, as well as its movement and dispersal towards the Indian landmass in the subsequent 72 h, resulting in a significant amount of transported dust that engulfed the west Indian region on 23–24 January. We utilize satellite observations and in situ measurements to quantify the ability of the forecasting system to predict the spatiotemporal characteristics of the dust storm. Notably, the AQEWS aerosol data assimilation process played a critical role in forecasting the anomalous increase of PM10 in several west Indian cities, as it significantly improved the initial conditions of PM10 by approximately 24–40 μg/m3 and PM2.5 concentration by approximately 30–38 μg/m3. Without assimilation, the system fails to capture the intensity, and pattern of variability of the dust storm. The mean bias of the 72-h prediction of PM10 was substantially reduced with assimilation, from −44% to 0.35% in Mumbai and from −57% to 13% in Gujarat. In selected regions, in situ measurements confirmed that the AQEWS adequately captured the peak intensity and pattern of the dust storm event. The AQEWS thus demonstrated improved forecasting skills in predicting an extreme dust event during winter and the adoption of chemical data assimilation enhanced the accuracy of the real-time air quality forecasting system.

Probing into the wintertime meteorology and particulate matter (PM2.5 and PM10) forecast over Delhi

The Indian Institute of Tropical Meteorology (IITM), in partnership with the National Center for Atmospheric Research (NCAR), has developed a high resolution (400 m) air-quality Early Warning System (EWS) for Delhi using an advanced approach of assimilating aerosol optical depth, fire emissions from a space-borne platform, and real-time aerosol observations from in-situ network in WRF-Chem model to produce a 72-h forecast. The present study summarizes the performance of EWS forecast and prevailing meteorological conditions for winter months of 2020–2021. We examined the performance of model-simulated meteorology by comparing against the in-situ observations. The model shows a positive bias in downwelling shortwave radiation (≈ 34 Wm−2) and warm bias in near-surface temperature (≈ 3 K). The simulated winds while having realistic magnitudes depict more southwesterly behavior compared to the observations. The model struggles to accurately predict Planetary Boundary Layer Height. The model realistically simulates the relationship between wind and air quality parameters. The air quality forecast from the model is found to be skillful in three different AQI categories, with accuracy > 88% for critical category events. The model also exhibits good statistical performance in predicting AQI, with low mean bias (¡ 8%), high correlation (¿ 0.68), and low variance (NMSE ¡ 0.09).

An innovative ensemble learning air pollution early-warning system for China based on incremental extreme learning machine

Air pollution has lots of adverse effects on industrial production and public life. Thus, it is an urgent task to construct an efficient air quality early-warning system to guide public life and production. This paper proposes an innovative air pollution early-warning system, including four main modules: clustering, preprocessing, forecasting and evaluation. In the clustering module, with the aim of building an efficient air pollution warning system, the air pollution situation of 31 provincial capitals is clustered and the study areas of the current study are selected based on the clustering result. A new data preprocessing algorithm is conducted to excavate the potential characteristics of the raw time series in the first place in the preprocessing module. Then, the length-changeable incremental extreme learning machine is used to forecast each component. In the evaluation module, the air quality is qualitatively analyzed by the fuzzy evaluation method. Moreover, the DM test and the SPA test are employed to test the accuracy of the forecasting model. The experimental results of eighteen data sets from three cities show that the hybrid air quality early-warning system establish in the study not only has higher accuracy and generalization ability than other benchmark models, but can provide sufficient air quality information, which is essential to control air pollution.

Evaluation of PM<sub>2.5</sub> Forecast using Chemical Data Assimilation in the WRF-Chem Model: A Novel Initiative Under the Ministry of Earth Sciences Air Quality Early Warning System for Delhi, India

Evaluation of PM<sub>2.5</sub> Forecast using Chemical Data Assimilation in the WRF-Chem Model: A Novel Initiative Under the Ministry of Earth Sciences Air Quality Early Warning System for Delhi, India

A novel hybrid air quality early-warning system based on phase-space reconstruction and multi-objective optimization: A case study in China

With the haze pollution occurred frequently in recent years, establishing an effective air quality early-warning system has become the top priority. Nowadays, researchers have provided numerous methods for air quality early-warning. However, the majority of studies ignores the significance of data preprocessing and air quality evaluation while designing an early-warning system, which leads to poor forecasting performance and insufficient information. A novel hybrid air quality early-warning system that consists of three modules: data preprocessing, forecasting and air quality evaluation module is designed in this paper. Aiming at extract chaotic characteristics of raw data, a new hybrid data preprocessing strategy is firstly developed to construct a more stable series of pollutant data for forecasting. Then a multi-objective grasshopper optimization algorithm is adopted in order to enhance the forecasting capability of accuracy and stability in the forecasting module. Moreover, a fuzzy evaluation module of air quality is proposed to provide comprehensive results for the system. Through the eighteen data sets’ experimental process, the results and discussions indicate that not only the forecasting method achieves higher accuracy and stronger stability than other comparison models, but also the evaluation module provides sufficient air quality information, which forms a scientific guidance to decision-makers against air pollution.

A novel combined forecasting system for air pollutants concentration based on fuzzy theory and optimization of aggregation weight

Effective forecasting of the air pollutant concentration is crucial for a robust air quality early-warning system and has both theoretical and practical significance. However, the accidental and cognitive uncertainty in the model selection or parameter setting of a single system will result in inaccurate and unstable forecasting results. Thus, in this paper, a novel fuzzy combination forecasting system based on the data preprocessing, fuzzy theory, and advanced optimization algorithm is proposed to improve the accuracy and stability of forecasting results. Based on the fuzzy theory and decorrelation maximization method, our proposed forecasting system can considering more information and maintaining the diversity of models. Moreover, Cuckoo Search algorithm applied in the system can determine the optimal weights for models aggregation. Several experiments based on PM2.5 and PM10 datasets in three cities are analyzed and discussed to verify the excellent performance of our proposed forecasting system, and the results indicate that the forecasting system outperforms others with respect to the accuracy, stability and generalization capabilities which are the basis of a robust air quality early-warning system in practice.

A Novel Air Quality Early-Warning System Based on Artificial Intelligence.

The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and important. Regretfully, previous research didn’t thoroughly explore not only air pollutant prediction but also air quality evaluation, and relevant research work is still scarce, especially in China. Therefore, a novel air quality early-warning system composed of prediction and evaluation was developed in this study. Firstly, the advanced data preprocessing technology Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with the powerful swarm intelligence algorithm Whale Optimization Algorithm (WOA) and the efficient artificial neural network Extreme Learning Machine (ELM) formed the prediction model. Then the predictive results were further analyzed by the method of fuzzy comprehensive evaluation, which offered intuitive air quality information and corresponding measures. The proposed system was tested in the Jing-Jin-Ji region of China, a representative research area in the world, and the daily concentration data of six main air pollutants in Beijing, Tianjin, and Shijiazhuang for two years were used to validate the accuracy and efficiency. The results show that the prediction model is superior to other benchmark models in pollutant concentration prediction and the evaluation model is satisfactory in air quality level reporting compared with the actual status. Therefore, the proposed system is believed to play an important role in air pollution control and smart city construction all over the world in the future.

An innovative hybrid air pollution early-warning system based on pollutants forecasting and Extenics evaluation

Air pollution poses a substantial threat to society and is considered one of the greatest environmental hazards for human beings. It is of great importance to develop air pollution early warning systems to alleviate the urgency and necessity of air quality monitoring and analysis. However, current early warning systems rarely focus on mining of pollutant characteristics and their corresponding scientific evaluation. In this study, we investigate an innovative hybrid air quality early warning system that comprises characteristics estimation, prediction, and evaluation. First, four different distribution functions with two estimation methods were applied for quarrying the characteristics of pollutants; Afterward, a hybrid forecasting model was proposed combined with an advanced data processing technique—a neural network and a new heuristic algorithm. For further mining the features of pollutants, two interval approaches as well as Extenics evaluation were utilized as indispensable components of the developed system. Simulation of pollutants series, including PM2.5, SO2, NO2, CO, and O3 are in line with lognormal distribution using certain parameters, and forecasting results are in good accordance with the empirical data using multiple criterion systems, i.e., MAE, MSE, RMSE, MAPE, MdAPE, FB, DA, and R2 for deterministic forecasting and with IFCP and IFNAW for interval forecasting. Eight deterministic forecasting criterion for pollutants forecasting measurement indicate that the developed early warning system can achieve good performance in terms of its accuracy and effectiveness. Additionally, the positive interval forecasting results and good precision of Extenics evaluation indicate the efficiency and scalability of the designed early warning system.

The study and application of a novel hybrid system for air quality early-warning

Air quality early-warning plays a vital role in improving air quality and human health, especially multi-step ahead air quality early-warning, which is significant for both citizens and environmental protection departments. However, most previous studies have only employed simple data decomposition to perform one-step forecasting and were aimed at enhancing forecasting accuracy or stability. Little research has improved these two standards simultaneously, leading to poor forecasting performance. Because of its significance, relevant research focused on multi-step ahead air quality early-warning is especially needed. Therefore, in this paper, a novel hybrid air quality early-warning system, which consists of four modules: data preprocessing module, optimization module, forecasting module and evaluation module, is proposed to perform multi-step ahead air quality early-warning. In this system, an effective data decomposition method called the modified complete ensemble empirical mode decomposition with adaptive noise is developed to effectively extract the characteristics of air quality data and to further improve the forecasting performance. Moreover, the hybrid Elman neural network model, optimized by the multi-objective salp swarm algorithm, is successfully developed in the forecasting module and simultaneously achieves high forecasting accuracy and stability. In addition, the evaluation module is designed to conduct a reasonable and scientific evaluation for this system. Three cities in China are employed to test the effectiveness of the proposed early-warning system, and the results reveal that the proposed early-warning system has superior ability in both accuracy and stability than other benchmark models and can be used as a reliable tool for multi-step ahead air quality early-warning.

Research and application of a novel hybrid air quality early-warning system: A case study in China

As one of the most serious meteorological disasters in modern society, air pollution has received extensive attention from both citizens and decision-makers. With the complexity of pollution components and the uncertainty of prediction, it is both critical and challenging to construct an effective and practical early-warning system. In this paper, a novel hybrid air quality early-warning system for pollution contaminant monitoring and analysis was proposed. To improve the efficiency of the system, an advanced attribute selection method based on fuzzy evaluation and rough set theory was developed to select the main pollution contaminants for cities. Moreover, a hybrid model composed of the theory of “decomposition and ensemble”, an extreme learning machine and an advanced heuristic algorithm was developed for pollution contaminant prediction; it provides deterministic and interval forecasting for tackling the uncertainty of future air quality. Daily pollution contaminants of six major cities in China were selected as a dataset to evaluate the practicality and effectiveness of the developed air quality early-warning system. The superior experimental performance determined by the values of several error indexes illustrated that the proposed early-warning system was of great effectiveness and efficiency.

Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model.

The worsening atmospheric pollution increases the necessity of air quality early warning systems (EWSs). Despite the fact that a massive amount of investigation about EWS in theory and practicality has been conducted by numerous researchers, studies concerning the quantification of uncertain information and comprehensive evaluation are still lacking, which impedes further development in the area. In this paper, firstly a comprehensive warning system is proposed, which consists of two vital indispensable modules, namely effective forecasting and scientific evaluation, respectively. For the forecasting module, a novel hybrid model combining the theory of data preprocessing and numerical optimization is first developed to implement effective forecasting for air pollutant concentration. Especially, in order to further enhance the accuracy and robustness of the warning system, interval forecasting is implemented to quantify the uncertainties generated by forecasts, which can provide significant risk signals by using point forecasting for decision-makers. For the evaluation module, a cloud model, based on probability and fuzzy set theory, is developed to perform comprehensive evaluations of air quality, which can realize the transformation between qualitative concept and quantitative data. To verify the effectiveness and efficiency of the warning system, extensive simulations based on air pollutants data from Dalian in China were effectively implemented, which illustrate that the warning system is not only remarkably high-performance, but also widely applicable.

Air quality early-warning system for cities in China

Air pollution has become a serious issue in many developing countries, especially in China, and could generate adverse effects on human beings. Air quality early-warning systems play an increasingly significant role in regulatory plans that reduce and control emissions of air pollutants and inform the public in advance when harmful air pollution is foreseen. However, building a robust early-warning system that will improve the ability of early-warning is not only a challenge but also a critical issue for the entire society. Relevant research is still poor in China and cannot always satisfy the growing requirements of regulatory planning, despite the issue's significance. Therefore, in this paper, a hybrid air quality early-warning system was successfully developed, composed of forecasting and evaluation. First, a hybrid forecasting model was proposed as an important part of this system based on the theory of “decomposition and ensemble” and combined with the advanced data processing technique, support vector machine, the latest bio-inspired optimization algorithm and the leave-one-out strategy for deciding weights. Afterwards, to intensify the research, fuzzy evaluation was performed, which also plays an indispensable role in the early-warning system. The forecasting model and fuzzy evaluation approaches are complementary. Case studies using daily air pollution concentrations of six air pollutants from three cities in China (i.e., Taiyuan, Harbin and Chongqing) are used as examples to evaluate the efficiency and effectiveness of the developed air quality early-warning system. Experimental results demonstrate that both the accuracy and the effectiveness of the developed system are greatly superior for air quality early warning. Furthermore, the application of forecasting and evaluation enables the informative and effective quantification of future air quality, offering a significant advantage, and can be employed to develop rapid air quality early-warning systems.