Urban Air Quality Prediction Research Articles

Enhancing urban air quality prediction using time-based-spatial forecasting framework.

Air quality forecasting plays a pivotal role in environmental management, public health and urban planning. This research presents a comprehensive approach for forecasting the Air Quality Index (AQI). The proposed Time-Based-Spatial (TBS) forecasting framework is integrated with spatial and temporal information using machine learning techniques on data collected from a wide range of cities. The TBS employs Convolutional Neural Networks (CNNs) to capture spatial dependencies based on normalized latitude and longitude coordinates of the cities. Simultaneously, time series model, specifically the ARIMA (AutoRegressive Integrated Moving Average)was employed to capture temporal dependencies using pollutant concentration readings over time. The dataset included information such as date, time, pollutant concentrations and AQI was further preprocessed and divided into training and testing sets. The CNN was configured to utilize the normalized latitude and longitude grid, while the ARIMA model concurrently processed the pollutant concentrations. The model was trained on the training dataset, and a 6hour forecast is generated for each test instance. The outcomes demonstrate the TBS model's ability to accurately predict AQI values. The integration of CNNs and time series model allowed for an clearer and deeper understanding of geographical and pollutant concentration factors that contribute to air quality variations.

Enhanced Jaya optimization for improving multilayer perceptron neural network in urban air quality prediction

AbstractThe multilayer perceptron (MLP) neural network is a widely adopted feedforward neural network (FNN) utilized for classification and prediction tasks. The effectiveness of MLP greatly hinges on the judicious selection of its weights and biases. Traditionally, gradient-based techniques have been employed to tune these parameters during the learning process. However, such methods are prone to slow convergence and getting trapped in local optima. Predicting urban air quality is of utmost importance to mitigate air pollution in cities and enhance the well-being of residents. The air quality index (AQI) serves as a quantitative tool for assessing the air quality. To address the issue of slow convergence and limited search space exploration, we incorporate an opposite-learning method into the Jaya optimization algorithm called EOL-Jaya-MLP. This innovation allows for more effective exploration of the search space. Our experimentation is conducted using a comprehensive 3-year dataset collected from five air quality monitoring stations. Furthermore, we introduce an external archive strategy, termed EOL-Archive-Jaya, which guides the evolution of the algorithm toward more promising search regions. This strategy saves the best solutions obtained during the optimization process for later use, enhancing the algorithm’s performance. To evaluate the efficacy of the proposed EOL-Jaya-MLP and EOL-Archive-Jaya, we compare them against the original Jaya algorithm and six other popular machine learning techniques. Impressively, the EOL-Jaya-MLP consistently outperforms all other methods in accurately predicting AQI levels. The MLP model’s adaptability to dynamic urban air quality patterns is achieved by selecting appropriate values for weights and biases. This leads to efficacy of our proposed approaches in achieving superior prediction accuracy, robustness, and adaptability to dynamic environmental conditions. In conclusion, our study shows the superiority of the EOL-Jaya-MLP over traditional methods and other machine learning techniques in predicting AQI levels, offering a robust solution for urban air quality prediction. The incorporation of the EOL-Archive-Jaya strategy further enhances the algorithm’s effectiveness, ensuring a more efficient exploration of the search space.

Deep learning based multimodal urban air quality prediction and traffic analytics

Urban activities, particularly vehicle traffic, are contributing significantly to environmental pollution with detrimental effects on public health. The ability to anticipate air quality in advance is critical for public authorities and the general public to plan and manage these activities, which ultimately help in minimizing the adverse impact on the environment and public health effectively. Thanks to recent advancements in Artificial Intelligence and sensor technology, forecasting air quality is possible through the consideration of various environmental factors. This paper presents our novel solution for air quality prediction and its correlation with different environmental factors and urban activities, such as traffic density. To this aim, we propose a multi-modal framework by integrating real-time data from different environmental sensors and traffic density extracted from Closed Circuit Television footage. The framework effectively addresses data inconsistencies arising from sensor and camera malfunctions within a streaming dataset. The dataset exhibits real-world complexities, including abrupt camera or station activations/deactivations, noise interference, and outliers. The proposed system tackles the challenge of predicting air quality at locations having no sensors or experiencing sensor failures by training a joint model on the data obtained from nearby stations/sensors using a Particle Swarm Optimization (PSO)-based merit fusion of the sensor data. The proposed methodology is evaluated using various variants of the LSTM model including Bi-directional LSTM, CNN-LSTM, and Convolutions LSTM (ConvLSTM) obtaining an improvement of 48%, 67%, and 173% for short-term, medium-term, and long-term periods, respectively, over the ARIMA model.

Combined model of air quality index forecasting based on the combination of complementary empirical mode decomposition and sequence reconstruction

One of the most important issues that cities face is air pollution. In this study, a novel integrated forecasting model of the air quality index (AQI) is suggested to carry out reliable prediction, providing useful references for urban air pollution control, public health construction, and residents' travel planning. Firstly, the original data is decomposed by the method of complementary set empirical mode decomposition (CEEMD), and the subsequences of different frequencies are formed. Secondly, the fuzzy entropy (FE) algorithm is used to reconstruct the subsequence. Then, the combined forecasting model is established, and different prediction methods are selected for different frequency subsequences. The new high-frequency sequences, low-frequency sequences, and trend sequences are predicted by the whale algorithm optimized long short term neural network (WOA-LSTM) and the extreme learning machine (ELM), respectively. Empirical analysis are carried out with the example of Beijing and Chengdu. The results indicated that: (1) The proposed CEEMD—FE—WOA-LSTM—ELM model effectively integrates the characteristics of the original sequence and has the highest prediction accuracy among all the comparison models. (2) It is necessary to preprocess the data, which can effectively extract data features. Taking Beijing as an example, compared with the non-decomposition model, after adding the decomposition algorithm, the prediction accuracy rate (PA) is increased by 8.55% on average, the RMSE is decreased by 10.36 on average, and the MAPE is decreased by 6.11% on average. (3) The overall prediction level and prediction accuracy can be effectively increased by applying various prediction methods for recombination sequences with various frequency. The research results can provide references for urban air quality prediction.

Research on the application of urban air quality prediction and prediction model under the background of big data

In the previous research on air quality prediction, the research on the problem is usually one-sided, and many problems are solved from a single time dimension. In the research of this problem, this paper starts from the time dimension and the space dimension respectively. Considering the temporal continuity and spatial diffusion of air pollutants, the prediction results of the two dimensions are dynamically combined. Comprehensive consideration of various factors to achieve better prediction results. In order to solve the problem that there are few air quality monitoring stations in cities and there is no monitoring data in a large number of areas, an air quality prediction model is proposed.

Multivariate Time Series Forecasting with Dilated Residual Convolutional Neural Networks for Urban Air Quality Prediction

Multivariate time series (MTS) forecasting is a research field that is gaining more and more importance as time series data generators proliferate in the growing era of Internet of Things. Deep learning architecture for MTS data has been and still a very active research area as there is no comprehensive comparative study of the different architectures, let alone a perfect architecture that can solve all types of time series modeling problem. In this paper, we start by highlighting time series analysis requirements, to propose a new model for conditional multivariate time series forecasting based on the lately introduced WaveNet architecture. Our model is composed of stacked residual causal dilated convolutions that provide large scope in time series history and foster learning of long-term dependencies. Parameterized skip connections allow catching early trends and properties while conditioning enables modeling the associations that exist between multivariate data. We used group normalization for its stability and independence from the batch size. We present a deep comparison of the structure, flexibility, flow control, memory consumption, robustness and stability capacities of our new model against those of recurrent neural networks state-of-the-art approaches—long-short-term-memory (LSTM) and gated recurrent unit (GRU). To assess the performances of our model, we conduct extensive experimental testing on the task of urban air quality prediction in Marrakesh city using six real-world multi-sensor and multivariate time series datasets. We compare the results of our model against the results of LSTM and GRU. Our experiments revealed that our proposed WaveNet-temporal-CNN outperform recurrent models ability to learn long-term dependencies in a time-efficient way.

Predicting intraurban PM2.5 concentrations using enhanced machine learning approaches and incorporating human activity patterns

Urban areas contribute substantially to human exposure to ambient air pollution. Numerous statistical prediction models have been used to estimate ambient concentrations of fine particulate matter (PM2.5) and other pollutants in urban environments, with some incorporating machine learning (ML) algorithms to improve predictive power. However, many ML approaches for predicting ambient pollutant concentrations to date have used principal component analysis (PCA) with traditional regression algorithms to explore linear correlations between variables and to reduce the dimensionality of the data. Moreover, while most urban air quality prediction models have traditionally incorporated explanatory variables such as meteorological, land use, transportation/mobility, and/or co-pollutant factors, recent research has shown that local emissions from building infrastructure may also be useful factors to consider in estimating urban pollutant concentrations. Here we propose an enhanced ML approach for predicting urban ambient PM2.5 concentrations that hybridizes cascade and PCA methods to reduce the dimensionality of the data-space and explore nonlinear effects between variables. We test the approach using different durations of time series air quality datasets of hourly PM2.5 concentrations from three air quality monitoring sites in different urban neighborhoods in Chicago, IL to explore the influence of dynamic human-related factors, including mobility (i.e., traffic) and building occupancy patterns, on model performance. We test 9 state-of-the-art ML algorithms to find the most effective algorithm for modeling intraurban PM2.5 variations and we explore the relative importance of all sets of factors on intraurban air quality model performance. Results demonstrate that Gaussian-kernel support vector regression (SVR) was the most effective ML algorithm tested, improving accuracy by 118% compared to a traditional multiple linear regression (MLR) approach. Incorporating the enhanced approach with SVR algorithm increased model performance up to 18.4% for yearlong and 98.7% for month-long hourly datasets, respectively. Incorporating assumptions for human occupancy patterns in dominant building typologies resulted in improvements in model performance by between 4% and 37%. Combined, these innovations can be used to improve the performance and accuracy of urban air quality prediction models compared to conventional approaches.

Building Confidence in Simulation: Applications of EasyVVUQ

AbstractValidation, verification, and uncertainty quantification (VVUQ) of simulation workflows are essential for building trust in simulation results, and their increased use in decision‐making processes. The EasyVVUQ Python library is designed to facilitate implementation of advanced VVUQ techniques in new or existing workflows, with a particular focus on high‐performance computing, middleware agnosticism, and multiscale modeling. Here, the application of EasyVVUQ to five very diverse application areas is demonstrated: materials properties, ocean circulation modeling, fusion reactors, forced human migration, and urban air quality prediction.

Research on Urban Air Quality Prediction Based on Ensemble Learning of XGBoost

In recent years, with the rapid development of China’s economy and the continuous improvement of people’s quality of life, air pollution caused by a large amount of energy consumption has become increasingly serious. Air quality index (AQI) has become an important basis to measure air quality. At present, the research on air quality assessment and prediction methods has become increasingly active at home and abroad, which is of great significance to guide people’s production and life. In this paper, Taking Shijiazhuang, Hebei Province as an example and using the XGBoost model of the machine learning ensemble algorithm, regression fitting was performed on the six pollutant concentrations that currently mainly affect air quality, and the hourly prediction of AQI was achieved.The trained model has lower mean absolute error (MAE) and higher correlation coefficient (R-square), which improves the prediction ability of urban air quality prediction, provides a new idea for urban air quality prediction, and has a broad application prospect in the future urban air quality prediction.

A state of the art regarding urban air quality prediction models

Urban pollution represents an increasing risk to residents of urban regions, particularly in large, over-industrialized cities knowing that the traffic is responsible for more than 25% of air gaseous pollutants and dust particles. Air quality modelling plays an important role in addressing air pollution control and management approaches by providing guidelines for better and more efficient air quality forecasting, along with smart monitoring sensor networks. The advances in technology regarding simulations, forecasting and monitoring are part of the new smart cities which offers a healthy environment for their occupants.

Linear and nonlinear modeling approaches for urban air quality prediction

In this study, linear and nonlinear modeling was performed to predict the urban air quality of the Lucknow city (India). Partial least squares regression (PLSR), multivariate polynomial regression (MPR), and artificial neural network (ANN) approach-based models were constructed to predict the respirable suspended particulate matter (RSPM), SO2, and NO2 in the air using the meteorological (air temperature, relative humidity, wind speed) and air quality monitoring data (SPM, NO2, SO2) of five years (2005–2009). Three different ANN models, viz. multilayer perceptron network (MLPN), radial-basis function network (RBFN), and generalized regression neural network (GRNN) were developed. All the five different models were compared for their generalization and prediction abilities using statistical criteria parameters, viz. correlation coefficient (R), standard error of prediction (SEP), mean absolute error (MAE), root mean squared error (RMSE), bias, accuracy factor (Af), and Nash–Sutcliffe coefficient of efficiency (Ef). Nonlinear models (MPR, ANNs) performed relatively better than the linear PLSR models, whereas, performance of the ANN models was better than the low-order nonlinear MPR models. Although, performance of all the three ANN models were comparable, the GRNN over performed the other two variants. The optimal GRNN models for RSPM, NO2, and SO2 yielded high correlation (between measured and model predicted values) of 0.933, 0.893, and 0.885; 0.833, 0.602, and 0.596; and 0.932, 0.768 and 0.729, respectively for the training, validation and test sets. The sensitivity analysis performed to evaluate the importance of the input variables in optimal GRNN revealed that SO2 was the most influencing parameter in RSPM model, whereas, SPM was the most important input variable in other two models. The ANN models may be useful tools in the air quality predictions.

Artificial neural network based vehicular pollution prediction model: a practical approach for urban air quality prediction

The achievement of local air quality management goals in densely populated built-up urban areas often requires short-term air quality plans or emission management policies. Vehicular pollution modelling is an effective tool in managing the vehicular exhaust emissions in urban environments. In the recent past, Artificial Neural Network (ANN) based vehicular pollution models are found to be useful alternatives to conventional deterministic and stochastic based models in predicting vehicular pollutants dispersion. This paper compares the prediction performance of the ANN-based short-term vehicular pollution model with some frequently used conventional models using real-time data at one of the densely populated built-up areas in the city of Delhi, India.

Foreshowing of the Western Pacific tropical cyclone track to PM10 air pollution episode in the Beijing area

By utilizing the air quality monitoring data and the NCEP reanalysis data, the relationship between the PM10 air pollution episode and synoptic situation is analyzed in the Beijing area. It is interesting to find that PM10 air pollution episode in and around the Beijing area is correlated with the Western Pacific tropical cyclone track to some extent, namely when a tropical cyclone lands southward to the Changjiang River, PM10 air pollution episode is not easy to take place generally in the Beijing area; but when a tropical cyclone moves northward and finally lands at the Korea Peninsula or the Japanese mainland, and under this condition the Beijing area is generally controlled by weak high or weak low for several days, PM10 air pollution episode often takes place in one day or several days. Above findings indicate that predicting the Western Pacific tropical cyclone track can foretell whether or not PM10 air pollution episode takes place in the Beijing area, which can offer a technique for urban air quality prediction and air pollution source management in the Beijing area.

A Bayesian hierarchical model for urban air quality prediction under uncertainty

Urban air quality is subject to the increasing pressure of urbanization, and, consequently, the potential impact of air quality changes must be addressed. A Bayesian hierarchical model was developed in this paper for urban air quality predication. Literature data on three pollutants and four external driving factors in Xiamen City, China, were studied. The air quality model structure and prior distributions of model parameters were determined by multivariate statistical methods, including correlation analysis, classification and regression trees (CART), hierarchical cluster analysis (CA), and discriminant analysis (DA). A multiple linear regression (MLR) equation was proposed to measure the relationship between pollutant concentrations and driving variables; and Bayesian hierarchical model was introduced for parameters estimation and uncertainty analysis. Model fit between the observed data and the modeled values was demonstrated, with mean and median values and two credible levels (2.5% and 97.5%). The average relative errors between the observed data and the mean values of SO 2, NO x , and dust fall were 6.81%, 6.79%, and 3.52%, respectively.