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Accurate air quality prediction is paramount in safeguarding public health and addressing air pollution control. However, previous studies often ignore the geographic similarity among different monitoring stations and face challenges in dynamically capturing different spatial–temporal relationships between stations. To address this, an air quality predictive learning approach incorporating the Third Law of Geography with SAM–CNN–Transformer is proposed. Firstly, the Third Law of Geography is incorporated to fully consider the geographical similarity among stations via a variogram and spatial clustering. Subsequently, a spatial–temporal attention convolutional network that combines the spatial attention module (SAM) with the convolutional neural network (CNN) and Transformer is designed. The SAM is employed to extract spatial–temporal features from the input data. The CNN is utilized to capture local information and relationships among each input feature. The Transformer is applied to capture time dependencies across long-distance time series. Finally, Shapley's analysis is employed to interpret the model factors. Numerous experiments with two typical air pollutants (PM2.5, PM10) in Haikou City show that the proposed approach has better comprehensive performance than baseline models. The proposed approach offers an effective and practical methodology for fine-grained non-stationary air quality predictive learning. [ABSTRACT FROM AUTHOR]

Air pollution control is one of the main challenges facing modern societies. Consequently, the estimation of population, affluence, and technology impacts on air pollution concentrations (STIRPAT modeling) has become the cornerstone of environmental decision-making. Spatial effects are not usually included in STIRPAT modeling of air pollution. However, space matters: accounting for spatial dependencies significantly improves the accuracy of estimates and forecasts, especially (or only) when dealing with small information units rather than with large ones (countries, large regions, provinces in China, counties and states in the USA, etc.). The latter scale is typical in the literature on air pollution due to the difficulties in finding data on its drivers at a true local scale. Accordingly, this paper has a double objective. The first is the estimation of a spatial panel data STIRPAT model, with the spatial units being both very small and also highly autonomous, developed municipalities. The second is to examine whether an environmental Kuznets curve relationship exists between income per capita and NOx concentrations. A case study has been carried out in the Autonomous Community of Madrid, Spain, at the municipal level. [ABSTRACT FROM AUTHOR]

The insufficient development of intelligent dynamic monitoring systems, which operate with big data, obstructs the control of traffic-related air pollution in regulated urban road networks. The study introduces mathematical models and presents a practical comparative assessment of pollutant emissions at urban intersections, with two typical modes of vehicle traffic combined, i.e., freely passing an intersection when the green signal appears and uniformly accelerated passing after a full stop at the stop line. Input data on vehicle traffic at regulated intersections were collected using real-time processing of video streams by Faster R-CNN neural network. Calculation models for different traffic flow patterns at a regulated intersection for dynamic monitoring of pollutant emissions were obtained. Statistical analysis showed a good grouping of intersections into single-type clusters and factor reduction of initial variables. Analysis will further allow us to control and minimize traffic-related emissions in urban road networks. A comparative analysis of pollutant emissions in relation to the basic speed of passing at the intersection of 30 km/h was performed according to the calculations of the mathematical models. When reducing the speed to 10 km/h (similar to a traffic jam), the amount of emissions increases 3.6 times over, and when increasing the speed to 50 km/h, the amount of emissions decreases by 2.3 times. Fuzzy logic methods allow us to make a comparative prediction of the amount of emissions when changing both the speed of traffic and the capacity of the intersection lanes. The study reveals the benefits of using a real-life measurement approach and provides the foundation for continuous monitoring and emission forecasting to control urban air quality and reduce congestion in the road network. [ABSTRACT FROM AUTHOR]

Ground level concentrations of nitrogen oxide (NOx) can act as an indicator of air quality in the urban environment. In cities with relatively good air quality, and where NOx concentrations rarely exceed legal limits, adverse health effects on the population may still occur. Therefore, detecting small deviations in air quality and deriving methods of controlling air pollution are challenging. This study presents different data analytical methods which can be used to monitor and effectively evaluate policies or measures to reduce nitrogen oxide (NOx) emissions through the detection of pollution episodes and the removal of outliers. This method helps to identify the sources of pollution more effectively, and enhances the value of monitoring data and exceedances of limit values. It will detect outliers, changes and trend deviations in NO2 concentrations at ground level, and consists of four main steps: classical statistical description techniques, statistical process control techniques, functional analysis and a functional control process. To demonstrate the effectiveness of the outlier detection methodology proposed, it was applied to a complete one-year NO2 dataset for a sub-urban site in Dublin, Ireland in 2013. The findings demonstrate how the functional data approach improves the classical techniques for detecting outliers, and in addition, how this new methodology can facilitate a more thorough approach to defining effect air pollution control measures. [ABSTRACT FROM AUTHOR]

Air pollution (mainly PM2.5) is one of the main environmental problems about air quality. Air pollution prediction and early warning is a prerequisite for air pollution prevention and control. However, it is not easy to accurately predict the long-term trend because the collected PM2.5 data have complex nonlinearity with multiple components of different frequency characteristics. This study proposes a hybrid deep learning predictor, in which the PM2.5 data are decomposed into components by empirical mode decomposition (EMD) firstly, and a convolutional neural network (CNN) is built to classify all the components into a fixed number of groups based on the frequency characteristics. Then, a gated-recurrent-unit (GRU) network is trained for each group as the sub-predictor, and the results from the three GRUs are fused to obtain the prediction result. Experiments based on the PM2.5 data from Beijing verify the proposed model, and the prediction results show that the decomposition and classification can develop the accuracy of the proposed predictor for air pollution prediction greatly. [ABSTRACT FROM AUTHOR]

Airborne particulate matter (PM) is a key air pollutant that affects human health adversely. Exposure to high concentrations of such particles may cause premature death, heart disease, respiratory problems, or reduced lung function. Previous work on particulate matter ( P M 2.5 and P M 10 ) was limited to specific areas. Therefore, more studies are required to investigate airborne particulate matter patterns due to their complex and varying properties, and their associated ( P M 10 and P M 2.5 ) concentrations and compositions to assess the numerical productivity of pollution control programs for air quality. Consequently, to control particulate matter pollution and to make effective plans for counter measurement, it is important to measure the efficiency and efficacy of policies applied by the Ministry of Environment. The primary purpose of this research is to construct a simulation model for the identification of a change point in particulate matter ( P M 2.5 and P M 10 ) concentration, and if it occurs in different areas of the world. The methodology is based on the Bayesian approach for the analysis of different data structures and a likelihood ratio test is used to a detect change point at unknown time (k). Real time data of particulate matter concentrations at different locations has been used for numerical verification. The model parameters before change point (θ) and parameters after change point (λ) have been critically analyzed so that the proficiency and success of environmental policies for particulate matter ( P M 2.5 and P M 10 ) concentrations can be evaluated. The main reason for using different areas is their considerably different features, i.e., environment, population densities, and transportation vehicle densities. Consequently, this study also provides insights about how well this suggested model could perform in different areas. [ABSTRACT FROM AUTHOR]