How BLUE is the Sky? Estimating the Air Quality Data in Beijing During the Blue Sky Day Period (2008-2012) by the Bayesian LSTM Approach

Over the last three decades, air pollution has become a major environmental challenge in many of the fast growing cities in China, including Beijing. Given that any long-term exposure to high-levels of air pollution has devastating health consequences, accurately monitoring and reporting air pollution information to the public is critical for ensuring public health and safety and facilitating rigorous air pollution and health-related scientific research. Recent statistical research examining China’s air quality data has posed questions regarding data accuracy, especially data reported during the Blue Sky Day (BSD) period (2000 – 2012), though the accuracy of publicly available air quality data in China has improved gradually over the recent years (2013 – 2017). To the best of our understanding, no attempt has been made to re-estimate the air quality data during the BSD period. In this paper, we put forward a machine-learning model to re-estimate the official air quality data during the BSD period of 2008 – 2012, based on the PM2.5 data of the Beijing US Embassy, and the proxy data covering Aerosol Optical Depth (AOD) and meteorology. Results have shown that the average re-estimated daily air quality values are respectively 64% and 61% higher than the official values, for air quality index (AQI) and AQI equivalent PM2.5, during the BSD period of 2008 to 2012. Moreover, the re-estimated BSD air quality data exhibit reduced statistical discontinuity and irregularity, based on our validation tests. The results suggest that the proposed data re-estimation methodology has the potential to provide more justifiable historical air quality data for evidence-based environmental decision-making in China

Design and validation of novel methods for long-term road traffic forecasting

132 p.Road traffic management is a critical aspect for the design and planning of complex urban transport networks for which vehicle flow forecasting is an essential component. As a testimony of its paramount relevance in transport planning and logistics, thousands of scientific research works have covered the traffic forecasting topic during the last 50 years. In the beginning most approaches relied on autoregressive models and other analysis methods suited for time series data. During the last two decades, the development of new technology, platforms and techniques for massive data processing under the Big Data umbrella, the availability of data from multiple sources fostered by the Open Data philosophy and an ever-growing need of decision makers for accurate traffic predictions have shifted the spotlight to data-driven procedures. Even in this convenient context, with abundance of open data to experiment and advanced techniques to exploit them, most predictive models reported in literature aim for shortterm forecasts, and their performance degrades when the prediction horizon is increased. Long-termforecasting strategies are more scarce, and commonly based on the detection and assignment to patterns. These approaches can perform reasonably well unless an unexpected event provokes non predictable changes, or if the allocation to a pattern is inaccurate.The main core of the work in this Thesis has revolved around datadriven traffic forecasting, ultimately pursuing long-term forecasts. This has broadly entailed a deep analysis and understanding of the state of the art, and dealing with incompleteness of data, among other lesser issues. Besides, the second part of this dissertation presents an application outlook of the developed techniques, providing methods and unexpected insights of the local impact of traffic in pollution. The obtained results reveal that the impact of vehicular emissions on the pollution levels is overshadowe

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

Design and validation of novel methods for long-term road traffic forecasting

132 p.Road traffic management is a critical aspect for the design and planning of complex urban transport networks for which vehicle flow forecasting is an essential component. As a testimony of its paramount relevance in transport planning and logistics, thousands of scientific research works have covered the traffic forecasting topic during the last 50 years. In the beginning most approaches relied on autoregressive models and other analysis methods suited for time series data. During the last two decades, the development of new technology, platforms and techniques for massive data processing under the Big Data umbrella, the availability of data from multiple sources fostered by the Open Data philosophy and an ever-growing need of decision makers for accurate traffic predictions have shifted the spotlight to data-driven procedures. Even in this convenient context, with abundance of open data to experiment and advanced techniques to exploit them, most predictive models reported in literature aim for shortterm forecasts, and their performance degrades when the prediction horizon is increased. Long-termforecasting strategies are more scarce, and commonly based on the detection and assignment to patterns. These approaches can perform reasonably well unless an unexpected event provokes non predictable changes, or if the allocation to a pattern is inaccurate.The main core of the work in this Thesis has revolved around datadriven traffic forecasting, ultimately pursuing long-term forecasts. This has broadly entailed a deep analysis and understanding of the state of the art, and dealing with incompleteness of data, among other lesser issues. Besides, the second part of this dissertation presents an application outlook of the developed techniques, providing methods and unexpected insights of the local impact of traffic in pollution. The obtained results reveal that the impact of vehicular emissions on the pollution levels is overshadowe

Air Quality Research Using Remote Sensing

Air pollution is a worldwide environmental hazard that poses serious consequences not only for human health and the climate but also for agriculture, ecosystems, and cultural heritage, among other factors. According to the WHO, there are 8 million premature deaths every year as a result of exposure to ambient air pollution. In addition, more than 90% of the world’s population live in areas where the air quality is poor, exceeding the recommended limits. On the other hand, air pollution and the climate co-influence one another through complex physicochemical interactions in the atmosphere that alter the Earth’s energy balance and have implications for climate change and the air quality. It is important to measure specific atmospheric parameters and pollutant compound concentrations, monitor their variations, and analyze different scenarios with the aim of assessing the air pollution levels and developing early warning and forecast systems as a means of improving the air quality and safeguarding public health. Such measures can also form part of efforts to achieve a reduction in the number of air pollution casualties and mitigate climate change phenomena. This book contains contributions focusing on remote sensing techniques for evaluating air quality, including the use of in situ data, modeling approaches, and the synthesis of different instrumentations and techniques. The papers published in this book highlight the importance and relevance of air quality studies and the potential of remote sensing, particularly that conducted from Earth observation platforms, to shed light on this topic

Spatio-temporal correlation of extreme climate indices and river flood discharges

The occurrence of floods is strongly related to specific climatic conditions that favor extreme precipitation events. Although the impact of precipitation and temperature patterns on river flows is a well discussed topic in hydrology, few studies have focused on the rainfall and temperature extremes in their relation with peak discharges. This work presents a comparative analysis of Climate Change Indices (ETCCDI) annual time series, calculated using the NorthWestern Italy Optimal Interpolation (NWIOI) dataset, and annual maximum flows in the Piedmont Region. The Spearman’s rank correlation was used to determine which indices are temporally correlated with peak discharges, allowing to hypothesize the main physical processes involved in the production of floods. The correlation hypothesis was verified with the Spearman’s rank correlation test, considering a Student’s t-distribution with a 5% significance level. Moreover, the influence of climate variability on the tendency of annual maximum discharges was examined by correlating trends of climate indices with trends of the discharge series. These were calculated using the Theil-Sen slope estimator and tested with the Mann-Kendall test at the 5% significance level. The results highlight that while extreme precipitation indices are highly correlated with extreme discharges at the annual timescale, the interannual changes of extreme discharges may be better explained by the interannual changes of the total annual precipitation. This suggests that projections of the annual precipitation may be used as covariates for non-stationary flood frequency analysis

Simulating urban soil carbon decomposition using local weather input from a surface model

Non peer reviewe