Assessing uncertainty and heterogeneity in machine learning-based spatiotemporal ozone prediction in Beijing-Tianjin- Hebei region in China

Accurate prediction of spatiotemporal ozone concentration is of great significance to effectively establish advanced early warning systems and regulate air pollution control. However, the comprehensive assessment of uncertainty and heterogeneity in spatiotemporal ozone prediction remains unknown. Here, we systematically analyze the hourly and daily spatiotemporal predictive performances using convolutional long short term memory (ConvLSTM) and deep convolutional generative adversarial network (DCGAN) models over the Beijing-Tianjin-Hebei region in China from 2013 to 2018. In extensive scenarios, our results show that the machine learning-based (ML-based) models achieve better spatiotemporal ozone concentration prediction performance with multiple meteorological conditions. A further comparison to the air pollution model-Nested Air Quality Prediction Modelling System (NAQPMS) and monitoring observations, the ConvLSTM model demonstrates the practical feasibility of identifying high ozone concentration distribution and capturing spatiotemporal ozone variation patterns at a high spatial resolution (here 15 km × 15 km)

Satellite-based PM2.5 Exposure Estimation and Health Impacts over China

Exposure to suspended fine particulate matter (PM2.5) has been proven to adversely impact public health through increased risk of cardiovascular and respiratory mortality. Assessing health impacts of PM2.5 and its long-term variations requires accurate estimates of large-scale exposure data. Such data include mass concentration and particle size, the latter of which may be an effect modifier on PM2.5 attributable health risks. The availability of these exposure data, however, is limited by sparse ground-level monitoring networks. In this dissertation, an optical-mass relationship was first developed based on aerosol microphysical characteristics for ground-level PM2.5 retrieval. This method quantifies PM2.5 mass concentrations with a theoretical basis, which can simultaneously estimate large-scale particle size. The results demonstrate the effectiveness and applicability of the proposed method and reveal the spatiotemporal distribution of PM2.5 over China. To explore the spatial variability and population exposure, particle radii of PM2.5 are then derived using the developed theoretical relationship along with a statistical model for a better performance. The findings reveal the prevalence of exposure to small particles (i.e. PM1), identify the need for in-situ measurements of particle size, and motivate further research to investigate the effects of particle size on health outcomes. Finally, the long-term impacts of PM2.5 on health and environmental inequality are assessed by using the satellite-retrieved PM2.5 estimates over China during 2005-2017. Premature mortality attributable to PM2.5 exposure increased by 31% from 2005 to 2017. For some causes of death, the burden fell disproportionately on provinces with low-to-middle GDP per capita. As a whole, this work contributes to bridging satellite remote sensing and long-term exposure studies and sheds light on an ongoing need to understand the effects of PM2.5, including both concentrations and other particle characteristics, on human health

Exploring Beijing's urban environment: air quality, haze effects and climate

Towards the end of the last century, significant increases in coal burning for electricity at power plants and for domestic heating purposes led to rapid increases in the levels of harmful gaseous and particulate pollutants in the atmosphere over urban areas in China. The concurrent rise in urban populations has resulted in an ever-increasing human health risk associated with the inhalation of ambient air pollution. Most notably, long- and short-term exposure to PM2.5, describing fine particles suspended in the atmosphere with a diameter ≤ 2.5 μm, is the fourth leading cause of premature death in China. Additionally, very high ambient concentrations of PM2.5 characterising haze pollution events can block a large fraction of incoming sunlight, impacting atmospheric chemistry and reducing heating of the urban surface which drives the mixing of near-surface air. Both of these effects can substantially impact urban air quality. Development of urban areas is also associated with the replacement of vegetative, rural surfaces with urban materials (e.g. concrete). This increases the amount of sunlight absorbed at the ground, reduces cooling associated with evaporation, and thus urban air temperatures are higher than adjacent rural areas. Heat released by human activities (anthropogenic heat) directly into the atmosphere further strengthens the urban -rural temperature contrast (known as the urban heat island). Extreme urban air temperatures, particularly in summer, reduce human thermal comfort and increase the heat-related mortality risk. The main objectives of this study are to (a) quantify the air pollution levels in Beijing in winter at street-scale resolution and understand the key influencing processes, (b) quantify the effects of haze-sunlight interactions on Beijing’s air quality in winter, and (c) understand the relative importance of urban materials and anthropogenic heat emissions in forming Beijing’s urban heat island in winter and summer. For the air quality-related objectives of this work, we use a computer-based model (ADMS-Urban) that replicates the release of air pollutants from a range of activities in urban areas, and accounts for the effects of local weather and chemical reactions on air pollution levels. The ADMS-Urban model can estimate air pollution level variations across very fine spatial extents (< 10 m). Comparisons between modelled and measured air pollutant concentrations inform on the model performance, revealing the success of our model configuration and whether further adjustments are required. To study Beijing’s climate, we use a recently developed variation of the ADMS-Urban model, the ADMS-Urban Temperature and Humidity model (ADMS-UHI). ADMS-UHI is used to calculate air temperature variations across neighbourhood extents (~ 100 m) due to surface thermal characteristics and anthropogenic heat release. The air quality model simulations require estimates of emission rates from roads. For this, we share emissions estimated at grid-level (3 km) resolutions across individual road sections; major roads (e.g. motorway) are apportioned more than minor roads (e.g. residential). Measured air pollutant concentrations are much higher than modelled levels at night, suggesting the emissions estimates are missing a significant evening source; this is most likely related to heavy duty diesel trucks that enter the city at night when travel restrictions are lifted. The model cannot replicate the measured PM2.5 spatial variability. This is likely due to the exclusion of emissions from explicitly represented point sources (e.g. large industry) in the model configuration due to a lack of information on their locations. Also, we assume background air pollutant levels are uniform across Beijing, which is unlikely across such a large urban area. Overall, the measured-modelled air pollutant concentration agreement is strong, indicating that the methodologies and materials implemented for this study can be successfully applied to urban areas elsewhere to help guide air pollution control policies. According to a number of previous modelling studies across China, haze-sunlight interactions have substantial effects on urban air quality. Therefore, to improve the accuracy of urban air quality assessments with ADMS-Urban across polluted megacities such as Beijing, the model, which currently does not account for the impacts of haze, should be adjusted accordingly. Therefore, we alter the modelled air pollution dispersion and chemistry processes to account for reduced sunlight reaching the surface on haze days and analyse the impacts on simulated near-surface air quality. Reduced surface heating on haze days suppresses the vertical mixing of air and therefore increases the modelled concentrations of primary air pollutants such as nitrogen oxides (NOx = NO + NO2). However, near-surface levels of the secondary air pollutant species O₃ reduces substantially due to (a) increased removal from reactions with NO, and (b) suppressed formation via sunlight interactions with NO₂. Measured-modelled O₃ agreement improves significantly when haze effects are considered. The final objective of this thesis is explored with ADMS-UHI. During the winter, the impact of anthropogenic heat emissions on the UHI far outweighs the effects of urban surface thermal properties. UHI intensities (urban-rural temperature difference) are much greater at night than during the day in both winter and summer. In summer, this is mostly related to the evening release of heat stored in the urban fabric throughout the day. Negative daytime UHI intensities in summer highlight the cooling effect of urban green space. Overall, this study provides useful information for urban planners aiming to reduce heat-related health risks across complex urban areas such as Beijing

An IoT enabled system for enhanced air quality monitoring and prediction on the edge

Air pollution is a major issue resulting from the excessive use of conventional energy sources in developing countries and worldwide. Particulate Matter less than 2.5 µm in diameter (PM2.5) is the most dangerous air pollutant invading the human respiratory system and causing lung and heart diseases. Therefore, innovative air pollution forecasting methods and systems are required to reduce such risk. To that end, this paper proposes an Internet of Things (IoT) enabled system for monitoring and predicting PM2.5 concentration on both edge devices and the cloud. This system employs a hybrid prediction architecture using several Machine Learning (ML) algorithms hosted by Nonlinear AutoRegression with eXogenous input (NARX). It uses the past 24 h of PM2.5, cumulated wind speed and cumulated rain hours to predict the next hour of PM2.5. This system was tested on a PC to evaluate cloud prediction and a Raspberry Pi to evaluate edge devices’ prediction. Such a system is essential, responding quickly to air pollution in remote areas with low bandwidth or no internet connection. The performance of our system was assessed using Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), coefficient of determination (R2), Index of Agreement (IA), and duration in seconds. The obtained results highlighted that NARX/LSTM achieved the highest R2 and IA and the least RMSE and NRMSE, outperforming other previously proposed deep learning hybrid algorithms. In contrast, NARX/XGBRF achieved the best balance between accuracy and speed on the Raspberry Pi

Book of short Abstracts of the 11th International Symposium on Digital Earth

The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium

Estimating PM2.5 Concentrations Based on MODIS AOD and NAQPMS Data over Beijing–Tianjin–Hebei

Accurately estimating fine ambient particulate matter (PM2.5) is important to assess air quality and to support epidemiological studies. To analyze the spatiotemporal variation of PM2.5 concentrations, previous studies used different methodologies, such as statistical models or neural networks, to estimate PM2.5. However, there is little research on full-coverage PM2.5 estimation using a combination of ground-measured, satellite-estimated, and atmospheric chemical model data. In this study, the linear mixed effect (LME) model, which used the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), meteorological data, normalized difference vegetation index (NDVI), and elevation data as predictors, was fitted for 2017 over Beijing&ndash;Tianjin&ndash;Hebei (BTH). The LME model was used to calibrate the PM2.5 concentration using the nested air-quality prediction modeling system (NAQPMS) simulated with ground measurements. The inverse variance weighting (IVW) method was used to fuse satellite-estimated and model-calibrated PM2.5. The results showed a strong agreement with ground measurements, with an overall coefficient (R2) of 0.78 and a root-mean-square error (RMSE) of 26.44 &mu;g/m3 in cross-validation (CV). The seasonal R2 values were 0.75, 0.62, 0.80, and 0.78 in the spring, summer, autumn, and winter, respectively. The fusion results supplement the lack of satellite estimates and can capture more detailed information than the NAQPMS model. Therefore, the results will be helpful for pollution process analyses and health-related studies

Biotechnology to Combat COVID-19

This book provides an inclusive and comprehensive discussion of the transmission, science, biology, genome sequencing, diagnostics, and therapeutics of COVID-19. It also discusses public and government health measures and the roles of media as well as the impact of society on the ongoing efforts to combat the global pandemic. It addresses almost every topic that has been studied so far in the research on SARS-CoV-2 to gain insights into the fundamentals of the disease and mitigation strategies. This volume is a useful resource for virologists, epidemiologists, biologists, medical professionals, public health and government professionals, and all global citizens who have endured and battled against the pandemic