Improvement of modelling human exposure to NO₂ in cities in China: the case of Guangzhou

Nitrogen dioxide (NO₂) is an air pollutant identified as a public health concern. Exposure to NO₃ is associated with a number of adverse respiratory health effects, and ultimately with premature mortality. It also contributes as a precursor to formation of tropospheric ozone (O₃) and ammonium nitrate fine particulate matter (PM₂.₅, particles with aerodynamic diameters <2.5 µm). Rapid economic growth, industrialization, and urbanization in China are leading to substantial adverse air quality issues, including high levels of annual mean NO₂ concentrations. It is important to quantify human exposure to NO₂ to evaluate its health impacts and to assess the effectiveness of mitigation approaches. Since 2013, the China National Environmental Monitoring Centre (CNEMC) has been implementing a nationwide monitoring network for the routine measurement of ambient air pollutant concentrations. Previous studies into population exposure used the monitor data as a proxy for human exposure. However, NO₂ concentrations within cities have shown high spatial variations. The monitoring network only provides concentrations at a limited number of discrete points, which is inadequate to describe the spatial variability of urban air pollution. New methods need to be developed to tackle these challenges. The overall aim of this PhD project is to explore modelling approaches for better estimating intra-urban variability of NO₂ for human exposure research in China, given the obstacles in data availability of monitored data, emission inventories, and other highly spatially resolved data in China. Guangzhou is chosen as an exemplar geographic domain. It is the third largest city in China, with a population of 14 million and an area of 7,433 km², and does not currently meet the Chinese air quality standard (GB 3095-2012) for NO2, which is set as 40 µg m-3 as an annual average. The Guangzhou local government has an air quality compliance plan that aspires to annual average NO₂ concentrations of 40 μg m⁻³ by 2020. Two modelling methods are widely used to simulate pollutant concentrations at relatively high spatial resolution within urban areas: dispersion modelling and land-use regression (LUR) modelling. Dispersion modelling aims to simulate the physical chemical processes that link the emissions of pollutants from sources and their transport and dispersion. Recently, urban dispersion models have been developed in Beijing, Shanghai, Chongqing, Hangzhou, Kunming, Hong Kong, Harbin, Lanzhou, Urumqi, Liaoning province, Jinan, Fushun, and Macao using ADMS and AERMOD. Substandard modelling results can arise due to insufficient monitor data and incomplete or inaccurate emission inventories. LUR relies on existing measurements to derive the statistical relationship between pollutant concentrations at a given location and predictor variables representing the emission and dispersion of air pollutants. An appropriately sized and designed monitoring network is an important component for the development of a robust LUR model. LUR models are now being applied to simulate pollutant concentrations with high spatial resolution in Chinese urban areas. Current challenges and future needs in employing LUR approaches were identified first in this PhD work. Details of twenty-four recent LUR models for NO₂ and PM₂.₅/PM₁₀ (particles with aerodynamic diameters <10 µm) were reviewed. LUR modelling in China is currently constrained by a scarcity of input data, especially air pollution monitor data. There is an urgent need for accessible archives of qualityassured measurement data and for higher spatial resolution proxy data for urban emissions, particularly in respect of traffic-related variables. The rapidly evolving nature of the Chinese urban landscape makes maintaining up-to-date land-use and urban morphology datasets essential for LUR models. Given the limited number of monitoring sites in Guangzhou and the geographical scale of the domain, an integrated modelling approach combining dispersion modelling with ADMS-Urban and LUR has been developed in this PhD work. ADMS-Urban was applied in Guangzhou using input data including emissions from the Multi-resolution Emission Inventory for China (MEIC), road geometry from OpenStreetMap, and hourly meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Concentrations of NO₂ were simulated by ADMS-Urban at 83 ‘virtual’ monitoring sites spanning the six districts in Guangzhou and weighted according to population (since the overall focus is estimation of population NO₂ exposure). The LUR model was validated against both the 83 virtual sites (adj R²: 0.96, RMSE: 5.48 μg m⁻³; LOOCV R²: 0.96, RMSE: 5.64 μg m⁻³) and, independently, against available observations (n = 11, R²: 0.63, RMSE: 18.0 μg m⁻³). The modelled population-weighted long-term average concentration of NO₂ across Guangzhou in 2017 was 52.5 μg m⁻³, which contributes an estimated 7,270 (6,960−7,620) attributable deaths. This hybrid modelling approach is then applied to explore the scale of emissions reductions necessary within the Guangzhou domain to achieve compliance with a number of different interpretations of an NO2 concentration target of 40 μg m⁻³. (The Guangzhou Ambient Air Quality Compliance Plan does not explicitly state how to practically assess compliance.) The modelling results show that achieving compliance requires different levels of emission reductions, depending on how the concentration target was defined; for example, to reduce the average concentration at all monitoring sites below 40 µg m⁻³, requires a 60% reduction of emissions from all source sectors. In contrast, to attain ≤40 µg m⁻³ concentration across the whole of Guangzhou requires a 90% emissions reduction. The impacts of the emissions reductions on NO₂-attributable premature mortality are also calculated and illustrate that use of a concentration value as a target does not fully convey the underlying health gains even when the target is not met. In the final part of this thesis, the findings and implications from the modelling studies are discussed in the context of current air quality management system in China. Whilst the results are based on detailed and consistent model results for the specific situation in Guangzhou, they are relevant for, and can provide evidence to, decision makers designing effective air pollution control policies in other fast-growing megacities in China and elsewhere globally. The challenges and limitations for the development of a highly spatial revolved model for human exposure are discussed

Achievements and Challenges in Improving Air Quality in China: Analysis of the Long-Term Trends from 2014 to 2022

Due to the implementation of air pollution control measures in China, air quality has significantly improved, although there are still additional issues to be addressed. This study used the long-term trends of air pollutants to discuss the achievements and challenges in further improving air quality in China. The Kolmogorov-Zurbenko (KZ) filter and multiple-linear regression (MLR) were used to quantify the meteorology-related and emission-related trends of air pollutants from 2014 to 2022 in China. The KZ filter analysis showed that PM2.5 decreased by 7.36 ± 2.92% yr&#x100000; 1, while daily maximum 8-h ozone (MDA8 O3) showed an increasing trend with 3.71 ± 2.89% yr&#x100000; 1 in China. The decrease in PM2.5 and increase in MDA8 O3 were primarily attributed to changes in emission, with the relative contribution of 85.8% and 86.0%, respectively. Meteorology variations, including increased ambient temperature, boundary layer height, and reduced relative humidity, also contributed to the reduction of PM2.5 and the enhancement of MDA8 O3. The emission-related trends of PM2.5 and MDA8 O3 exhibited continuous decrease and increase, respectively, from 2014 to 2022, while the variation rates slowed during 2018–2020 compared to that during 2014–2017, highlighting the challenges in further improving air quality, particularly in simultaneously reducing PM2.5 and O3. This study recommends reducing NH3 emissions from the agriculture sector in rural areas and transport emissions in urban areas to further decrease PM2.5 levels. Addressing O3 pollution requires the reduction of O3 precursor gases based on site-specific atmospheric chemistry considerations

Advances in air quality research – current and emerging challenges

This review provides a community\u27s perspective on air quality research focusing mainly on developments over the past decade. The article provides perspectives on current and future challenges as well as research needs for selected key topics. While this paper is not an exhaustive review of all research areas in the field of air quality, we have selected key topics that we feel are important from air quality research and policy perspectives. After providing a short historical overview, this review focuses on improvements in characterizing sources and emissions of air pollution, new air quality observations and instrumentation, advances in air quality prediction and forecasting, understanding interactions of air quality with meteorology and climate, exposure and health assessment, and air quality management and policy. In conducting the review, specific objectives were (i) to address current developments that push the boundaries of air quality research forward, (ii) to highlight the emerging prominent gaps of knowledge in air quality research, and (iii) to make recommendations to guide the direction for future research within the wider community. This review also identifies areas of particular importance for air quality policy. The original concept of this review was borne at the International Conference on Air Quality 2020 (held online due to the COVID 19 restrictions during 18–26 May 2020), but the article incorporates a wider landscape of research literature within the field of air quality science. On air pollution emissions the review highlights, in particular, the need to reduce uncertainties in emissions from diffuse sources, particulate matter chemical components, shipping emissions, and the importance of considering both indoor and outdoor sources. There is a growing need to have integrated air pollution and related observations from both ground-based and remote sensing instruments, including in particular those on satellites. The research should also capitalize on the growing area of low-cost sensors, while ensuring a quality of the measurements which are regulated by guidelines. Connecting various physical scales in air quality modelling is still a continual issue, with cities being affected by air pollution gradients at local scales and by long-range transport. At the same time, one should allow for the impacts from climate change on a longer timescale. Earth system modelling offers considerable potential by providing a consistent framework for treating scales and processes, especially where there are significant feedbacks, such as those related to aerosols, chemistry, and meteorology. Assessment of exposure to air pollution should consider the impacts of both indoor and outdoor emissions, as well as application of more sophisticated, dynamic modelling approaches to predict concentrations of air pollutants in both environments. With particulate matter being one of the most important pollutants for health, research is indicating the urgent need to understand, in particular, the role of particle number and chemical components in terms of health impact, which in turn requires improved emission inventories and models for predicting high-resolution distributions of these metrics over cities. The review also examines how air pollution management needs to adapt to the above-mentioned new challenges and briefly considers the implications from the COVID-19 pandemic for air quality. Finally, we provide recommendations for air quality research and support for policy

Advances in air quality research – current and emerging challenges

© Author(s) 2022. This work is distributed under the Creative Commons Attribution 4.0 License. https://creativecommons.org/licenses/by/4.0/This review provides a community’s perspective on air quality research focusing mainly on developmentsover the past decade. The article provides perspectives on current and future challenges as well asresearch needs for selected key topics. While this paper is not an exhaustive review of all research areas in thefield of air quality, we have selected key topics that we feel are important from air quality research and policy perspectives. After providing a short historical overview, this review focuses on improvements in characterizingsources and emissions of air pollution, new air quality observations and instrumentation, advances in air qualityprediction and forecasting, understanding interactions of air quality with meteorology and climate, exposure andhealth assessment, and air quality management and policy. In conducting the review, specific objectives were(i) to address current developments that push the boundaries of air quality research forward, (ii) to highlightthe emerging prominent gaps of knowledge in air quality research, and (iii) to make recommendations to guidethe direction for future research within the wider community. This review also identifies areas of particular importancefor air quality policy. The original concept of this review was borne at the International Conferenceon Air Quality 2020 (held online due to the COVID 19 restrictions during 18–26 May 2020), but the articleincorporates a wider landscape of research literature within the field of air quality science. On air pollutionemissions the review highlights, in particular, the need to reduce uncertainties in emissions from diffuse sources,particulate matter chemical components, shipping emissions, and the importance of considering both indoor andoutdoor sources. There is a growing need to have integrated air pollution and related observations from bothground-based and remote sensing instruments, including in particular those on satellites. The research shouldalso capitalize on the growing area of low-cost sensors, while ensuring a quality of the measurements which areregulated by guidelines. Connecting various physical scales in air quality modelling is still a continual issue,with cities being affected by air pollution gradients at local scales and by long-range transport. At the same time,one should allow for the impacts from climate change on a longer timescale. Earth system modelling offers considerablepotential by providing a consistent framework for treating scales and processes, especially where thereare significant feedbacks, such as those related to aerosols, chemistry, and meteorology. Assessment of exposureto air pollution should consider the impacts of both indoor and outdoor emissions, as well as application of moresophisticated, dynamic modelling approaches to predict concentrations of air pollutants in both environments.With particulate matter being one of the most important pollutants for health, research is indicating the urgentneed to understand, in particular, the role of particle number and chemical components in terms of health impact,which in turn requires improved emission inventories and models for predicting high-resolution distributions ofthese metrics over cities. The review also examines how air pollution management needs to adapt to the abovementionednew challenges and briefly considers the implications from the COVID-19 pandemic for air quality.Finally, we provide recommendations for air quality research and support for policy.Peer reviewe

Integrated human exposure to air pollution

The book “Integrated human exposure to air pollution” aimed to increase knowledge about human exposure in different micro-environments, or when citizens are performing specific tasks, to demonstrate methodologies for the understanding of pollution sources and their impact on indoor and ambient air quality, and, ultimately, to identify the most effective mitigation measures to decrease human exposure and protect public health. Taking advantage of the latest available tools, such as internet of things (IoT), low-cost sensors and a wide access to online platforms and apps by the citizens, new methodologies and approaches can be implemented to understand which factors can influence human exposure to air pollution. This knowledge, when made available to the citizens, along with the awareness of the impact of air pollution on human life and earth systems, can empower them to act, individually or collectively, to promote behavioral changes aiming to reduce pollutants’ emissions. Overall, this book gathers fourteen innovative studies that provide new insights regarding these important topics within the scope of human exposure to air pollution. A total of five main areas were discussed and explored within this book and, hopefully, can contribute to the advance of knowledge in this field

Air Pollution Meteorology

Although air pollution is usually linked with human activities, natural processes may also lead to major concentrations of hazardous substances in the low atmosphere. Pollutant levels may be reduced when emissions can be controlled. However, the impact of meteorological variables on the concentrations measured may be noticeable, and these variables cannot be controlled. This book is devoted to the influence of meteorological processes on the pollutant concentrations recorded in the low atmosphere. Measurements, cycles, statistical procedures, as well as specific variables such as the synoptic pattern, temperature inversion, or the calculation of back-trajectories, are considered in the studies included in this book to highlight the relationship between air pollution and meteorological variables. In addition, the state of the art of this subject following meteorological scales, from micro to macro-scale, is presented. Consequently, this book focuses on applied science and seeks to further current knowledge of what contribution meteorological processes make to the concentrations measured in order to achieve greater control over air pollution

Interpreting changes in anthropogenic emissions underlying abrupt changes in observed air quality using surface and satellite observations and a chemical transport model

Effective air quality policy is hindered by inaccurate estimates of precursor emissions, unvalidated, sparse or absent monitoring networks, and uncertain formation pathways of air pollution. Of particular concern are regions with severe air pollution, such as northern China and, large cities in South and Southeast Asia, and large cities in the world with high anthropogenic emissions. This work makes use of field campaign measurements, reference network measurements, satellite observations and a chemical transport model (CTM) to address these knowledge gaps in these regions. In the Beijing-Tianjin-Hebei region (BTH) in northern China, the Chinese government implemented strict emission control measures in autumn-winter 2017/2018 to address fine particulate matter (PM2.5) pollution. PM2.5 reduction targets were met, so these controls are now adopted in other parts of China, even though the relative role of emission controls and meteorology was not assessed. Surface observations of air quality from monitoring networks (validated against field campaign measurements) and the GEOS-Chem CTM were used after addressing large biases in the regional bottom-up anthropogenic emission inventory for China. According to the model, emission controls accounted for less than half (at most 43%) the decline in total PM2.5 while most (57%) was due to interannual variability in meteorology. Specifically, a deeper planetary boundary layer, stronger winds, and lower relative humidity during the emission control period. Emission controls alone would not achieve the PM2.5 reduction targets of 15-25% in this region. Cities in South and Southeast Asia are developing rapidly, but routine, up-to-date and publicly available inventories of emissions are lacking for this region. Nitrogen oxides (NOx) emissions in cities are important precursors to health-hazardous PM2.5 and tropospheric ozone (O3) where it is a greenhouse gas. NOx lifetimes and emissions over 10 large cities in South and Southeast Asia in 2019 were obtained by applying an exponentially modified Gaussian (EMG) approach with a wind rotation technique to the nitrogen dioxide (NO2) tropospheric vertical column densities (VCDs) from the high spatial resolution TROPOspheric Monitoring Instrument (TROPOMI). Annual averaged NOx emissions range from 100 mol s-1 for Delhi, Dhaka and Singapore. This is comparable to the range of emissions estimates for polluted cities in China. Bottom-up NOx emissions from a widely used publicly available global inventory exceed the top-down estimates for most cities. The discrepancy is >100% for Chennai, Singapore and Jakarta. It was only possible to estimate top-down monthly NOx estimates for 3 cities, due to issues with the line density fitting parameters at these fine temporal scales. These ranged from 63 to 148 mol s-1 for Singapore (annual mean 114 mol s-1), 44 to 109 mol s-1 for Jakarta (68 mol s-1), and 26 to 67 mol s-1 for Manila (53 mol s-1). Month-to-month variability is absent in the bottom-up emission estimates. The discrepancies identified in this work need to be resolved to ensure the development of effective policies. Abrupt changes in air quality during COVID-19 lockdowns presented an opportunity to investigate changes in observed PM2.5, NOx and O3 pollution due to interventions. Surface observations of air quality in 11 cities worldwide were analysed. Observed NO2 decreased substantially at urban background and roadside sites in all the cities, by 10-60% at urban background sites, and by 29 53% at roadside sites. In contrast, observed O3 increased in all cities after the lockdowns, by 16-167% at urban background sites and by 20-156% at roadside sites. The percentage changes in observed PM2.5 are -39 to 153% at urban background sites, -41 to 108% at roadside sites, and -34 to 165% at rural sites. But by comparing observations in 2020 to those in 2016-2019 during the equivalent periods, results here demonstrated that the observations of air quality alone cannot represent the changes in emissions due to COVID-19 lockdowns as the impact of meteorology should be considered. Findings in this thesis demonstrate the application of observations from multiple platforms, innovative analytical techniques, and an advanced chemical transport model to abrupt changes in air quality in time and space to better understand air pollution precursor emissions and formation pathways and to interpret the relative contribution from changes in emissions and meteorology. Such information is vital for developing well-informed environmental policies

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

Air Pollution Control and Sustainable Development

This book brings together the latest research findings on the state of air pollution control and its impact on economic growth in different countries. The book has substantial content and rich discussion. It is suitable for students and researchers at different levels to learn the status of air pollution, governance policies and their effects, and the relationship between pollution control and economic growth in countries around the world