Air Quality Prediction System Research Articles

A multi-task stations cooperative air quality prediction system for sustainable development

In recent years, A series of environmental problems caused by air pollution have attracted widespread attention. Air quality forecasting has become an indispensable part of people’s daily life. However, the traditional air quality prediction (AQP) model WRF-CMAQ simulation system faces several challenges: (1) the fuzziness of pollutant formation mechanism; (2) the hardness of integrating the features of meteorological conditions; (3) the difficulty of cooperating among monitoring stations. To deal with these challenges, we propose a multi-task station cooperative air quality prediction (MTSC-AQP) system for sustainable development. The MTSC-AQP system contains three modules: air quality analysis, a WRF-LSTM module for initial pollutant prediction, and multi-station cooperative AQP. Through air quality analysis, it can calculate the air quality index (AQI) and analyze the correlation between pollutants and meteorological conditions. Then, the WRF-LSTM module integrates spatio-temporal multi-source data to forecast the initial concentration of pollutants. Finally, the system incorporates the gravity model to predict the final AQI. Extensive experiments conducted on real-world datasets show the effectiveness of forecasting the AQI by using the MTSC-AQP system.

Design and Enhancement of a Fog-Enabled Air Quality Monitoring and Prediction System: An Optimized Lightweight Deep Learning Model for a Smart Fog Environmental Gateway.

Effective air quality monitoring and forecasting are essential for safeguarding public health, protecting the environment, and promoting sustainable development in smart cities. Conventional systems are cloud-based, incur high costs, lack accurate Deep Learning (DL)models for multi-step forecasting, and fail to optimize DL models for fog nodes. To address these challenges, this paper proposes a Fog-enabled Air Quality Monitoring and Prediction (FAQMP) system by integrating the Internet of Things (IoT), Fog Computing (FC), Low-Power Wide-Area Networks (LPWANs), and Deep Learning (DL) for improved accuracy and efficiency in monitoring and forecasting air quality levels. The three-layered FAQMP system includes a low-cost Air Quality Monitoring (AQM) node transmitting data via LoRa to the Fog Computing layer and then the cloud layer for complex processing. The Smart Fog Environmental Gateway (SFEG) in the FC layer introduces efficient Fog Intelligence by employing an optimized lightweight DL-based Sequence-to-Sequence (Seq2Seq) Gated Recurrent Unit (GRU) attention model, enabling real-time processing, accurate forecasting, and timely warnings of dangerous AQI levels while optimizing fog resource usage. Initially, the Seq2Seq GRU Attention model, validated for multi-step forecasting, outperformed the state-of-the-art DL methods with an average RMSE of 5.5576, MAE of 3.4975, MAPE of 19.1991%, R2 of 0.6926, and Theil's U1 of 0.1325. This model is then made lightweight and optimized using post-training quantization (PTQ), specifically dynamic range quantization, which reduced the model size to less than a quarter of the original, improved execution time by 81.53% while maintaining forecast accuracy. This optimization enables efficient deployment on resource-constrained fog nodes like SFEG by balancing performance and computational efficiency, thereby enhancing the effectiveness of the FAQMP system through efficient Fog Intelligence. The FAQMP system, supported by the EnviroWeb application, provides real-time AQI updates, forecasts, and alerts, aiding the government in proactively addressing pollution concerns, maintaining air quality standards, and fostering a healthier and more sustainable environment.

DeepFogAQ: A fog-assisted decentralized air quality prediction and event detection system

Air pollution is a pervasive environmental and public health concern, prompting the imperative development of predictive systems to facilitate proactive interventions. The complexity of predicting air pollution arises from intricate factors, including the accumulation of pollutants, traffic dynamics, and industrial emissions. Traditional methodologies, reliant on historical or real-time data analysis, often encounter limitations in providing comprehensive and accurate solutions to this multifaceted problem. To address the existing problem, we propose a novel fog-assisted decentralized air quality prediction and event detection system (DeepFogAQ) for managing air pollution of future cities. We integrate Deep Learning (DL), Fog Computing (FC), Complex Event Processing (CEP), and virtualization technologies within the architecture of DeepFogAQ. Specifically, for predicting pollutant concentrations, we employ Transformers, CNN-LSTM, GRU, and RFR models. Additionally, we construct Fog and Cloud layers based on container-based virtualization technology. To demonstrate the feasibility of the system, the developed ML/DL models were run on DeepFogAQ and alarm levels for future air quality were derived. In this way, both the success of the prediction models and the validity of the architecture were ensured. Experimental results showed that Transformers is the most successful model in air quality prediction and event detection. As a result, the proposed DeepFogAQ architecture has the potential to offer a powerful alternative to decision-makers to solve the air pollution problem with its decentralized, scalable, and fault-tolerant structure.

Assessing Real-Time Health Impacts of outdoor Air Pollution through IoT Integration

Air pollution constitutes a significant global challenge in both public health and the environment, particularly for countries undergoing industrialization and transitioning from low- to middle-income economies. This study aims to investigate the feasibility and effectiveness of a real-time air quality prediction system based on data collected from Internet of Things (IoT) sensors to help people and public institutions track and manage atmospheric pollution. The primary objective of this study was to investigate whether an IoT-based approach can provide accurate and continuous real-time air quality forecasting. The standard dataset provided by the Indian government was analyzed using regression, traditional Long-Short-Term Memory (LTSM), and bidirectional LSTM (BLSTM) models to evaluate their performance on multivariate air quality features. The results show that the proposed BLSTM model outperformed the other models in minimizing RMSE errors and avoiding overfitting.

HYAQP: A Hybrid Meta-Heuristic Optimization Model for Air Quality Prediction Using Unsupervised Machine Learning Paradigms

In the current decade, presence of air pollution leads towards serious health conditions, including respiratory ailments, cardiovascular disorders, and lung cancer, which impacts both the lifespan and overall well-being of individuals. Moreover, the air pollution has the potential to have detrimental effects on the environment, resulting in destruction to ecosystems, decreased agricultural output and so on. Emission control systems, better fuels, stronger laws, energy efficiency improvements, and renewable energy promotion are helping industries reduce air pollution. To monitor the quality of air methods such as Particulate Matter (PM2.5), PM10, Ozone (O3), and other meteorological indicators exists. These measures pose real-time air quality however it fails to predict air quality. Predicting air quality helps reduce air pollution by enabling timely interventions and preventive measures to mitigate pollution peaks. Thus, in this research a hybrid version of optimization algorithm namely Hybrid Air Quality Prediction system (HYAQP) which is a combination of k-means clustering algorithm and meta-heuristic algorithm Sine Cosine Algorithm (SCA) is proposed. The HYAQP holds SCA integrated with k-means algorithm to find optimal cluster centroid for grouping the air data into three clusters good, poor, and moderate quality. Then the cluster which is nearer to the test instance is found and the instances present in those clusters are passed to K-Nearest Neighbor Regressor (K-NNR). Comparing HYAQP on mean absolute error it outperforms 62.9% than Multiple Linear Regression (MLR), 58.5% than Support Vector Regression (SVR), 45.5% than Vanilla-Long Short-Term Memory (Vanilla-LSTM), 44.4% than Sparrow Search Algorithm based-LSTM (SSA-LSTM) and 53.8% than K-Nearest Neighbor (KNN).

High-resolution WRF forecasts in the SmartAQ system: Evaluation of the meteorological forcing used for PMCAMx predictions in an urban area

High-resolution modeling approaches are becoming a necessary step for enhancing regional air quality monitoring and forecasting. This study first evaluates the meteorological driver of the WRF- PMCAMx 1 × 1 km2 SmartAQ air quality prediction system and then assesses the impact of meteorological errors on the PM2.5 predictions. The comprehensive multi-variable verification on several timescales appraises the suitability of such a high-resolution modeling system in predicting the surface weather parameters in a major coastal urban area while gaining valuable insights into the model performance. The highest agreement between the observed and modelled values has been found for the actinometric and the thermodynamic variables, with the exception of the soil moisture which was systematically overestimated. WRF largely captures the variability of the observed values for the dynamic variables, showing better skill in the warm season, with a systematic overestimation though. The overall ability in predicting the rain events, important for the wet deposition of atmospheric pollutants is approximately 50%. Wind speed forecast errors contribute to the PM2.5 prediction errors, regardless of season and day/night hours. The impact of the wind errors is larger at almost all stations in the cold season when local emissions are elevated.

A Secure and Energy-efficient Framework for Air Quality Prediction Using Smart Sensors and ISHO-DCNN

Background: The World Health Organization (WHO) reported that Air pollution (AP) is prone to the highest environmental risk and has caused numerous deaths. Polluted air has many constituents where Particulate Matter (PM) is majorly reported as a global concern. Currently, the most crucial challenges faced by the globe are the identification and treatment of augmenting AP. The air pollution level was indicated by the Air Quality Index (AQI). It is affected by the concentrations of several pollutants in the air. Many pollutants in the air are harmful to human health. Thus, an efficient prediction system is required. Many security problems and lower classification accuracy are faced by them even though several prediction systems have been formed. A secure air quality prediction system (AQPS) centered upon the energy efficiency of smart sensing is proposed in this paper to overcome these issues. From disparate sensor nodes, the input data is initially amassed in the proposed work. The gathered data is stored in the temporary server. Next, the air-polluted data of the temporary server is offered to the AQPS, wherein preprocessing of the input data along with classification is executed. Methods: Utilizing the Improved Spotted Hyena Optimization-based Deep Convolution Neural Network (ISHO-DCNN) algorithm, the classification is executed. Utilizing the Repetitive Data Coding Based Huffman Encoding (RDC-HE) method, the polluted data attained from the classified output is compressed and encrypted by employing the American Standard Code for Information Interchange based Elliptical Curve Cryptography (ASCII-ECC) method. Results: Afterward, the encrypted and compressed data is saved in the Cloud Server (CS). Finally, for notifying about the AP, the decrypted and decompressed data is offered to the Base Stations (BS). Conclusion: The proposed work is more effective when analogized to the prevailing methods as denoted by the experimental outcomes. Higher accuracy of 97.14% and precision of 91.44% were obtained by the proposed model. Further, lower Encryption Time (ET) and Decryption Time (DT) of 0.565584 sec and 0.005137 sec were obtained by the model.

Assessment and Prediction of Air Quality Level Using ARIMA Model: A Case Study of Surat City, Gujarat State, India

Air quality has recently been a huge concern as it directly affects people’s lives. An air quality level assessment and prediction system is essential to keep track of air quality. Therefore, developing an efficient air quality assessment and prediction system has become one of the most important concerns. In the present work air quality level of Surat city, India is assessed and predicted for the period from 2020 to 2023 using the Autoregressive integrated moving average (ARIMA) model. Experimental results show that the ARIMA model outperforms the other models. According to the findings, the maximum quantity of SO2 and NO2 present in the air in 2020 is 37 mm and 18 mm, respectively, with a maximum of 27 mm and 31 mm in 2021. Thus, we can observe that even though SO2 has reduced a bit, the amount of NO2 has increased, thus degrading the quality of air.

Air quality measurement, prediction and warning using transfer learning based IOT system for ambient assisted living

PurposeIndoor air quality monitoring is extremely important in urban, industrial areas. Considering the devastating effect of declining quality of air in major part of the countries like India and China, it is highly recommended to monitor the quality of air which can help people with respiratory diseases, children and elderly people to take necessary precautions and stay safe at their homes. The purpose of this study is to detect air quality and perform predictions which could be part of smart home automation with the use of newer technology.Design/methodology/approachThis study proposes an Internet-of-Things (IoT)-based air quality measurement, warning and prediction system for ambient assisted living. The proposed ambient assisted living system consists of low-cost air quality sensors and ESP32 controller with new generation embedded system architecture. It can detect Indoor Air Quality parameters like CO, PM2.5, NO2, O3, NH3, temperature, pressure, humidity, etc. The low cost sensor data are calibrated using machine learning techniques for performance improvement. The system has a novel prediction model, multiheaded convolutional neural networks-gated recurrent unit which can detect next hour pollution concentration. The model uses a transfer learning (TL) approach for prediction when the system is new and less data available for prediction. Any neighboring site data can be used to transfer knowledge for early predictions for the new system. It can have a mobile-based application which can send warning notifications to users if the Indoor Air Quality parameters exceed the specified threshold values. This is all required to take necessary measures against bad air quality.FindingsThe IoT-based system has implemented the TL framework, and the results of this study showed that the system works efficiently with performance improvement of 55.42% in RMSE scores for prediction at new target system with insufficient data.Originality/valueThis study demonstrates the implementation of an IoT system which uses low-cost sensors and deep learning model for predicting pollution concentration. The system is tackling the issues of the low-cost sensors for better performance. The novel approach of pretrained models and TL work very well at the new system having data insufficiency issues. This study contributes significantly with the usage of low-cost sensors, open-source advanced technology and performance improvement in prediction ability at new systems. Experimental results and findings are disclosed in this study. This will help install multiple new cost-effective monitoring stations in smart city for pollution forecasting.

Complementary ensemble empirical mode decomposition and independent recurrent neural network model for predicting air quality index

Establishing a scientific and effective air quality prediction model is of great scientific value and practical significance for protecting people’s health and promoting social harmony and stability. However, existing prediction models have certain shortcomings in various aspects. To address these shortcomings, this paper combines different methods to achieve better prediction accuracy. Outlier analysis is done for air quality index (AQI) using an isolation forest algorithm. An air quality prediction system that consists of data preprocessing, optimization, prediction, and modification is established. The complementary ensemble empirical mode decomposition (CEEMD), modified particle swarm optimization (MPSO) algorithm, independent recurrent neural network (IndRNN), and nonlinear correction strategy are employed for the prediction. We select five cities with different AQI as the experiment sites, and three experiments are designed to test the accuracy of the prediction model. The results reveal that (1) by using CEEMD data decomposition technology to deal with the non-stationarity and nonlinearity of the original data, the prediction accuracy of the original cyclic neural network model can be improved by about 15%. (2) The prediction system with the CEEMD-MPSO-IndRNN model as the core prediction module and with a nonlinear error correction strategy has good scalability and robustness for air quality prediction. (3) The performance of the air quality prediction model constructed with systematic thought is better than other comparable models, and it can effectively predict the time series data of different cities and frequencies.

Spatiotemporal data partitioning for distributed random forest algorithm: Air quality prediction using imbalanced big spatiotemporal data on spark distributed framework

Spatiotemporal air quality datasets are typically collected hourly in monitoring stations deployed non-uniformly across a metropolitan city. These datasets are not only big, which poses challenges on the storage and processing capacity of centralized computing systems but also imbalanced and spatially heterogeneous, which may result in biased air quality prediction. To address these challenges, we designed and developed a parallel air quality prediction system equipped with a spatiotemporal data partitioning method, a distributed machine learning algorithm, Hadoop’s distributed data storage platform and its resource scheduler/manager, and Spark’s efficient and in-memory execution environment, which is suitable for running iterative algorithms, e.g., machine learning. Our proposed spatiotemporal partitioning method accounted for imbalance and spatial heterogeneity features of big air quality data in predictive models, which comply with the load-balancing requirement of distributed computing systems. Distributed Random Forest algorithm in the H2O library of the Spark framework was selected as the distributed machine learning algorithm to develop the air quality predictive model. This algorithm is an ensemble forest with algorithm-level adjustments to perform as efficiently as possible for big imbalanced datasets. An application of the parallel quality prediction system for Tehran, Iran showed that the parallel prediction system had considerable speedup gain and improved both the overall accuracy and class precision of air quality prediction when working with imbalanced big spatiotemporal air quality datasets. A future research direction is to add data streaming and visualization functions to the system to provide rapid and reliable air quality prediction for supporting environmental health management. • A parallel air quality prediction system based on Spark and Hadoop frameworks. • A spatiotemporal big data partitioning method based on location and year of data. • Implemented H2O Distributed Random Forest Algorithm using Sparkling Water library. • Achieved overall accuracy of 68% and precision improvements for minority classes. • Observed an increasing trend in speedup gain when adding more parallel processors.

A novel air quality prediction and early warning system based on combined model of optimal feature extraction and intelligent optimization

An effective air pollution prediction is of great significance to prevent and control air pollution and protect the health of residents. In order to improve the prediction accuracy of PM2.5, an innovative PM2.5 concentration prediction and early warning system based on optimal feature extraction and intelligent optimization is developed in this study. First, a feedback variational modal decomposition algorithm is designed to decompose the PM2.5 concentration sequence and fuzzy entropy is used to reconstruct the patterns of similar complexity. Then, Copula entropy is used to select the influencing factors with a high impact on PM2.5. Next, the reconstructed components and influencing factors are inputted to three individual prediction models, including long short-term memory neural network, gated recurrent unit neural network, and temporal convolutional network, for training and multi-step short-term prediction. The results of the individual prediction models are nonlinearly combined by Gaussian process regression which is optimized by the multi-objective grey wolf optimization algorithm. Finally, the prediction results of different reconstructed components are nonlinearly integrated to obtain the final PM2.5 prediction results. In an empirical study of two Chinese cities, the combined prediction model proposed in this study outperformed the other six comparative models in terms of prediction accuracy and stability. The experimental results prove that the hybrid prediction model proposed in this paper can make an effective prediction and early warnings of air pollution.

An integrated air quality modeling system coupling regional-urban and street models in Beijing

Recently, ozone (O3) pollution has become a significant problem for Beijing owing to its high traffic volume. To improve the simulation of street-scale nitrogen oxides (NOx) and O3, an integrated air quality modeling system coupling regional urban/street (IAQMS-street) was developed for Beijing. A weather research and forecasting atmospheric model, regional nested air quality prediction system, model of urban network of intersection canyons and highways, and a real-time on-road emission model were coupled by a downscaling method. A two-week simulation with high O3 concentrations (September 25, 2020 to October 8, 2020) was conducted to evaluate the impact of high-resolution traffic emissions and the street-scale simulation by three sensitivity scenarios. The results showed that IAQMS-street improved the model performance for NO, NO2, and O3 compared to regional models. The correlation coefficients (R) and normal mean bias (NMB) increased from ~0.3 to ~0.6 and from −83.3% to 14.8% for NO, respectively. For O3, the R increased by ~10%, and the NMB decreased from 48.7% to −4.4%. In particular, the model performance for O3, NO, and NO2 in high-concentration periods was significantly improved by IAQMS-street. Both high-resolution vehicle emissions and street-scale simulations contributed to this improvement. Our results showed that IAQMS-street provides a more realistic tool for simulating and resolving finer urban photochemical pollution heterogeneity in Beijing.

An intelligent fuzzy and IoT-aware air quality prediction and monitoring system using CRF and Bi-LSTM

Recently, air pollution has been increasing drastically in the majority of metropolitan cities around the world. This is necessary to reduce air pollution, and we propose a new air quality prediction system to predict air quality and pollution levels in different seasons in Beijing, China. The proposed air quality model applies a preliminary data preprocess to get exact data, a newly proposed conditional random field (CRF), and a fuzzy rule-based data grouping algorithm (CRF-FRDGA) to group the data according to the different seasonal data by applying the necessary rules, the standard bidirectional LSTM (Bi-LSTM) for performing an effective classification and prediction process. The PM2.5 concentration in Beijing, China, is forecasted season-wise for the next five years. Various experiments have been done to prove the capability of the proposed air quality prediction system and proved better than the existing works in prediction accuracy.

An Intelligent Fuzzy and IoT aware Air Quality Prediction and Monitoring System using CRF and Bi-LSTM

An Intelligent Fuzzy and IoT aware Air Quality Prediction and Monitoring System using CRF and Bi-LSTM

Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City Sensing Applications.

The sparse data in PM2.5 air quality monitoring systems is frequently happened on large-scale smart city sensing applications, which is collected via massive sensors. Moreover, it could be affected by inefficient node deployment, insufficient communication, and fragmented records, which is the main challenge of the high-resolution prediction system. In addition, data privacy in the existing centralized air quality prediction system cannot be ensured because the data which are mined from end sensory nodes constantly exposed to the network. Therefore, this paper proposes a novel edge computing framework, named Federated Compressed Learning (FCL), which provides efficient data generation while ensuring data privacy for PM2.5 predictions in the application of smart city sensing. The proposed scheme inherits the basic ideas of the compression technique, regional joint learning, and considers a secure data exchange. Thus, it could reduce the data quantity while preserving data privacy. This study would like to develop a green energy-based wireless sensing network system by using FCL edge computing framework. It is also one of key technologies of software and hardware co-design for reconfigurable and customized sensing devices application. Consequently, the prototypes are developed in order to validate the performances of the proposed framework. The results show that the data consumption is reduced by more than 95% with an error rate below 5%. Finally, the prediction results based on the FCL will generate slightly lower accuracy compared with centralized training. However, the data could be heavily compacted and securely transmitted in WSNs.

Improvement of chemical initialization in the air quality forecast system in North Macedonia, based on WRF-Chem model

Urban air quality is determined by a complex interaction of factors associated with anthropogenic emissions, atmospheric circulation, and geographic factors. Most of the urban-present pollution aerosols and trace gases are toxic to human health and responsible for damage of flora, fauna, and materials. The air quality prediction system based on state-of-the-art Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) has been configured and designed for North Macedonia. An extensive set of experiments have been performed with different model settings to forecast simultaneously the weather and air quality over the country. The initial results and the finding from other similar studies suggest that chemical initialization plays a significant role in a more accurate, both qualitative and quantitative forecast and assessment of urban air pollution. The main objective of the present research is to develop and test for a novel chemical initialization input in the air quality forecast system in North Macedonia. It is performed using ensemble technique in respect to treatment of the mobile emissions data using scaling factors. The WRF-Chem prediction has shown a high sensitivity to different scaling methods. While scaling of the overall mobile annual emissions tends to produce some discrepancies regarding the PM10 concentration level (overestimation during summer and underestimation during winter), an improved approach that utilizes scaling, in a wider range, only the mobile emissions originated from household heating offers the possibility of more detailed parameter fitting. The verification results indicate that the best accuracy across all scores for the winter months was achieved when scaling up the baseline pollutant input using a higher factor, while in the other seasons, the best results were achieved when scaling down the baseline pollutant emissions by a significant factor. Taking all into account, we can conclude that the seasonal variation in the pollutant input to the atmosphere is a significant factor in simulating the pollution in this region. Therefore, these seasonal variations must be taken into account when fitting the pollutant emission input to any model.

A Period-Aware Hybrid Model Applied for Forecasting AQI Time Series

An accurate, reliable and stable air quality prediction system is conducive to the public health and management of atmospheric ecological environment; therefore, many models, individual or hybrid, have been implemented widely to deal with the prediction problem. However, many of these models do not take into consideration or extract improperly the period information in air quality index (AQI) time series, which impacts the models’ learning efficiency greatly. In this paper, a period extraction algorithm is proposed by using a Luenberger observer, and then a novel period-aware hybrid model combined the period extraction algorithm and tradition time series models is build to exploit the comprehensive forecasting capacity to the AQI time series with nonlinear and non-stationary noise. The hybrid model requires a multi-phase implementation. In the first step, the Luenberger observer is used to estimate the implied period function in the one-dimensional AQI series, and then the analyzed time series is mapped to the period space through the function to obtain the period information sub-series of the original series. In the second step, the period sub-series is combined with the original input vector as input vector components according to the time points to establish a new data set. Finally, the new data set containing period information is applied to train the traditional time series prediction models. Both theoretical proof and experimental results obtained on the AQI hour values of Beijing, Tianjin, Taiyuan and Shijiazhuang in North China prove that the hybrid model with period information presents stronger robustness and better forecasting accuracy than the traditional benchmark models.

Development of Korean Air Quality Prediction System version 1 (KAQPS v1) with focuses on practical issues

Abstract. For the purpose of providing reliable and robust air quality predictions, an air quality prediction system was developed for the main air quality criteria species in South Korea (PM10, PM2.5, CO, O3 and SO2). The main caveat of the system is to prepare the initial conditions (ICs) of the Community Multiscale Air Quality (CMAQ) model simulations using observations from the Geostationary Ocean Color Imager (GOCI) and ground-based monitoring networks in northeast Asia. The performance of the air quality prediction system was evaluated during the Korea-United States Air Quality Study (KORUS-AQ) campaign period (1 May–12 June 2016). Data assimilation (DA) of optimal interpolation (OI) with Kalman filter was used in this study. One major advantage of the system is that it can predict not only particulate matter (PM) concentrations but also PM chemical composition including five main constituents: sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), organic aerosols (OAs) and elemental carbon (EC). In addition, it is also capable of predicting the concentrations of gaseous pollutants (CO, O3 and SO2). In this sense, this new air quality prediction system is comprehensive. The results with the ICs (DA RUN) were compared with those of the CMAQ simulations without ICs (BASE RUN). For almost all of the species, the application of ICs led to improved performance in terms of correlation, errors and biases over the entire campaign period. The DA RUN agreed reasonably well with the observations for PM10 (index of agreement IOA =0.60; mean bias MB =-13.54) and PM2.5 (IOA =0.71; MB =-2.43) as compared to the BASE RUN for PM10 (IOA =0.51; MB =-27.18) and PM2.5 (IOA =0.67; MB =-9.9). A significant improvement was also found with the DA RUN in terms of bias. For example, for CO, the MB of −0.27 (BASE RUN) was greatly enhanced to −0.036 (DA RUN). In the cases of O3 and SO2, the DA RUN also showed better performance than the BASE RUN. Further, several more practical issues frequently encountered in the air quality prediction system were also discussed. In order to attain more accurate ozone predictions, the DA of NO2 mixing ratios should be implemented with careful consideration of the measurement artifacts (i.e., inclusion of alkyl nitrates, HNO3 and peroxyacetyl nitrates – PANs – in the ground-observed NO2 mixing ratios). It was also discussed that, in order to ensure accurate nocturnal predictions of the concentrations of the ambient species, accurate predictions of the mixing layer heights (MLHs) should be achieved from the meteorological modeling. Several advantages of the current air quality prediction system, such as its non-static free-parameter scheme, dust episode prediction and possible multiple implementations of DA prior to actual predictions, were also discussed. These configurations are all possible because the current DA system is not computationally expensive. In the ongoing and future works, more advanced DA techniques such as the 3D variational (3DVAR) method and ensemble Kalman filter (EnK) are being tested and will be introduced to the Korean air quality prediction system (KAQPS).

Analysis and Forecast of Urban Air Quality Based on BP Neural Network

Abstract The rapid economic development has led to the declining quality of the atmospheric environment. At present, my country is facing a very serious problem of atmospheric environmental pollution. Accurate prediction of air quality plays a vital role in the realization of air pollution control by environmental protection departments. Based on the historical air pollution concentration data, this paper establishes a BP neural network model to learn the statistical law of air pollutant values to realize the prediction of air quality in the future. Through the analysis of the target of air quality prediction, the design of an air quality prediction method based on BP neural network is designed. This method includes four stages: air pollutant concentration data collection, data processing, air quality index calculation, and prediction network construction. The experimental results show that the air quality prediction method based on BP neural network designed and implemented in this paper, combined with the developed air quality prediction system, can effectively predict the recent changes in air quality and various air pollutant concentrations. By collecting the concentration data of air pollutants and learning the changes of air pollutants to achieve air quality prediction, it provides a quantitative reference for government environmental protection departments to achieve air pollution control.