A Bayesian LSTM model to evaluate the effects of air pollution control regulations in Beijing, China

© 2020 Elsevier Ltd Rapid socio-economic development and urbanization have resulted in serious deterioration in air-quality in many world cities, including Beijing, China. This study attempts to examine the effectiveness of air pollution control regulations implemented in Beijing during 2008–2019 through a data-driven regulatory intervention analysis. Our proposed Bayesian deep learning model utilizes proxy data including Aerosol Optical Depth (AOD) and meteorology as well as socio-economic data, while accounting for confounding effects via propensity score estimation. Our results show that air pollution control regulatory measures implemented in China and Beijing during 2008–2019 reduced PM2.5 pollution in Beijing by 11 % on average. After the introduction of Action Plan for Clean Air in China and Beijing in late 2013, as compared to the hypothetical PM2.5 concentration (without any regulatory interventions), the estimated PM2.5 reduction increased dramatically from 15 % in 2015 to 44 % in 2018. Our results suggest that Beijing's air quality has improved gradually over the past decade, though the annual PM2.5 pollution still exceeds the WHO threshold. In this regard, the air pollution control regulations introduced in Beijing and China tend to become more effective after 2015, suggesting a 2-year time lag before the stringent air pollution control regulations starting from 2013 takes any strong positive effects. Moreover, as compared to the air pollution control regulations introduced before 2013, newly introduced policy-making governance, which couples the policy-makings of the local jurisdictions with that of the central government, and the new policy measures that tackle the vested interests of the local stakeholders in Beijing and its nearby cities, alongside with the stringent local and national air pollution control regulations and plans, should help reduce air pollution and promote healthy living in Beijing over the longer term.This research is supported in part by the General Research Fund of the Research Grants Council of Hong Kong, under Grant No. 17620920

Spatiotemporal Deep Learning 모델을 기반으로 한 도시 전역의 대기 오염 보간과 예측

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2019. 2. 차상균.대기 오염은 대도시에서 가장 큰 문제 중 하나이다. 많은 국가들은 주요 도시 주변에 대기 오염 모니터링 센터를 건설하여 대기 오염 물질을 수집하고 해당 지역의 시민들에게 대기 오염을 경고한다. 그러나 도시에서의 대기 오염은 균일하지 않으며 시공간 (spatiotemporal)적인 문제이다. 대기 오염은 위치 (공간적 특성)과 시각 (시간적 특성)에 따라 달라진다. 따라서, 도시 전체의 대기 오염 보간과 예측은 시민들이 시간과 공간에 대해 대기의 질을 파악하고, 나아가 건강에 대한 위협을 제거하기 위한 필요 조건이다. 대기 오염은 도시 전역의 여러 시공간적 요인에 의해 영향을 받는 것으로 알려져 있다. 그 중, 기상이 대기 오염에 가장 큰 영향을 주는 것으로 인식되고 있다. 그 외에, 교통량은 대기 오염의 주요 원인인 도로의 차량 밀도를 반영한다. 평균 주행 속도는 도시 대기 오염에 영향을 준다고 판단되는 교통 체증을 나타낸다. 마지막으로, 외부 대기 오염원은 도시 대기 오염 문제의 근원 중 하나라고 주장된다. 본 논문에서는 서울시의 대기 오염 데이터, 기상 데이터, 교통량, 평균 주행 속도와 같은 많은 시공간적 데이터와 서울의 대기 오염에 영향을 준다고 알려진 중국의 3개 지방(베이징, 상하이, 산동)의 대기 오염 데이터를 제시하였다. 대기 오염에 대한 최근의 연구에서는 특정 위치와 시간의 대기 오염 예측 모델을 구축하려고 시도해왔다. 그러나 대부분 연속되지 않은 위치에대한 대기 오염을 예측하거나 직접 만든 공간 및 시간적 특성을 사용하는 데 중점을 두었다. 최근 CNN (Convolutional Neural Network), RNN (Recurrent Neural Network) 및 LSTM (Long-Short Term Memory)과 같은 딥러닝 모델이 공간 및 시간 관련 문제에서 우수하다고 알려져있다. 본 논문에서는 CNN과 LSTM을 결합한 ConvLSTM (Convolutional Long-Short Term Memory) 모델을 제안하였으며, 이를 통해 데이터의 공간 및 시간적 특성을 효율적으로 처리하고 최근의 다른 연구 결과보다 뛰어난 성능을 달성하였다.Air pollution is one of the most concerns of big cities. Many countries in the world have constructed air quality monitoring stations around major cities to collect air pollutants and make the warning to urban citizens about the air pollution around them. However, air pollution is not uniform in the city, but it is a spatiotemporal problem. It changes by locations (spatial feature) and by time (temporal feature). Consequently, citywide air pollution interpolation and prediction is a requirement of urban people to know the air quality through time and spaces to eliminate the health risks. Moreover, air pollution is affected by many spatiotemporal factors throughout the whole city. Among them, meteorology is recognized to be one the most significant effects to air pollution. Besides that, traffic volume reflects the density of vehicles on roads which is the primary cause of air pollution. Average driving speed indicates the traffic congestion which also reasonably influences air pollution over the city. Finally, external air pollution sources from outside areas are claimed to be the reason contributing to a city's air pollution problem. In this thesis, we present many spatiotemporal datasets collected over Seoul city, Korea such as air pollution data, meteorological data, traffic volume, average driving speed, and air pollution of 3 China areas like Beijing, Shanghai, Shandong, which are known to have the effect to Seoul's air pollution. Recent research in air pollution has tried to build models to predict air pollution by locations and in the future time. Nonetheless, they mostly focused on predicting air pollution in discrete locations or used hand-crafted spatial and temporal features. Recently, Deep learning models such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long-Short Term Memory (LSTM) are known to be superior in spatial and temporal relating problems. In this thesis, we propose the usage of Convolutional Long-Short Term Memory (ConvLSTM) model, a combination of CNN and LSTM, which efficiently manipulates the spatial and temporal features of the data and outperforms other recent research.1 INTRODUCTION 1 1.1 Air pollution description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Citywide Air pollution Interpolation and Prediction . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Spatiotemporal datasets introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Thesis contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8 2 RELATED WORK 11 2.1 Spatiotemporal Air pollution interpolation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2 Machine Learning/Neural Networks based Air pollution prediction models . . . .12 2.3 Spatiotemporal Deep Learning models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3 Spatiotemporal Deep Learning Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16 3.1 CNN and LSTM models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16 3.2 ConvLSTM model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.3 Air Pollution Interpolation and Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4 EXPERIMENTS AND EVALUATIONS 29 4.1 Baselines description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.2 Experiments and Evaluations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.2.1 Air pollution Interpolation: experiments and evaluations . . . . . . . . . . . . . . . . . 34 4.2.2 Air pollution Forecasting: experiments and evaluations . . . . . . . . . . . . . . . . . . 41 5 CONCLUSIONS AND FUTURE WORK 45 5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46Maste

The State-of-the-Art in Air Pollution Monitoring and Forecasting Systems using IoT, Big Data, and Machine Learning

The quality of air is closely linked with the life quality of humans, plantations, and wildlife. It needs to be monitored and preserved continuously. Transportations, industries, construction sites, generators, fireworks, and waste burning have a major percentage in degrading the air quality. These sources are required to be used in a safe and controlled manner. Using traditional laboratory analysis or installing bulk and expensive models every few miles is no longer efficient. Smart devices are needed for collecting and analyzing air data. The quality of air depends on various factors, including location, traffic, and time. Recent researches are using machine learning algorithms, big data technologies, and the Internet of Things to propose a stable and efficient model for the stated purpose. This review paper focuses on studying and compiling recent research in this field and emphasizes the Data sources, Monitoring, and Forecasting models. The main objective of this paper is to provide the astuteness of the researches happening to improve the various aspects of air polluting models. Further, it casts light on the various research issues and challenges also.Comment: 30 pages, 11 figures, Wireless Personal Communications. Wireless Pers Commun (2023

Urban PM2.5 concentration prediction via attention-based CNN–LSTM.

Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the effects of featured states at different times in the past on future PM2.5 concentration, and most fail to effectively simulate the temporal and spatial dependencies of PM2.5 concentration at the same time. With this consideration, we propose a deep learning-based method, AC-LSTM, which comprises a one-dimensional convolutional neural network (CNN), long short-term memory (LSTM) network, and attention-based network, for urban PM2.5 concentration prediction. Instead of only using air pollutant concentrations, we also add meteorological data and the PM2.5 concentrations of adjacent air quality monitoring stations as the input to our AC-LSTM. Hence, the spatiotemporal correlation and interdependence of multivariate air quality-related time-series data are learned by the CNN-LSTM network in AC-LSTM. The attention mechanism is applied to capture the importance degrees of the effects of featured states at different times in the past on future PM2.5 concentration. The attention-based layer can automatically weigh the past feature states to improve prediction accuracy. In addition, we predict the PM2.5 concentrations over the next 24 h by using air quality data in Taiyuan city, China, and compare it with six baseline methods. To compare the overall performance of each method, the mean absolute error (MAE), root-mean-square error (RMSE), and coecient of determination (R2) are applied to the experiments in this paper. The experimental results indicate that our method is capable of dealing with PM2.5 concentration prediction with the highest performance

Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks

Accurate and timely air quality and weather predictions are of great importance to urban governance and human livelihood. Though many efforts have been made for air quality or weather prediction, most of them simply employ one another as feature input, which ignores the inner-connection between two predictive tasks. On the one hand, the accurate prediction of one task can help improve another task's performance. On the other hand, geospatially distributed air quality and weather monitoring stations provide additional hints for city-wide spatiotemporal dependency modeling. Inspired by the above two insights, in this paper, we propose the Multi-adversarial spatiotemporal recurrent Graph Neural Networks (MasterGNN) for joint air quality and weather predictions. Specifically, we first propose a heterogeneous recurrent graph neural network to model the spatiotemporal autocorrelation among air quality and weather monitoring stations. Then, we develop a multi-adversarial graph learning framework to against observation noise propagation introduced by spatiotemporal modeling. Moreover, we present an adaptive training strategy by formulating multi-adversarial learning as a multi-task learning problem. Finally, extensive experiments on two real-world datasets show that MasterGNN achieves the best performance compared with seven baselines on both air quality and weather prediction tasks.Comment: 9 pages, 6 figure