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)

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􀀀 1, while daily maximum 8-h ozone (MDA8 O3) showed an increasing trend with 3.71 ± 2.89% yr􀀀 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

Statistical and machine learning modelling of UK surface ozone

In addition to atmospheric observations, numerical models are crucial to understand the impacts of human activities on the environment, from attributing poor air quality to assessing climate change impacts. While process-based models, such as chemistry transport models (CTMs), are widely used, recent data science advances enable greater use of statistical and machine learning methods as alternatives to describe and predict atmospheric composition. State-of-the-art data science methods can be faster to run than CTMs and used at high temporal and spatial resolutions due to codebase efficiencies. This thesis focuses on modelling UK surface ozone and its drivers (high levels of which are detrimental to human and plant health) through the development and novel application of sophisticated statistical and machine learning techniques. Motivated by possible adverse effect of climate change on ozone concentrations, a temperature-dependent Extreme Value Analysis is used to explore the probability, magnitude, and frequency of extreme ozone events over recent decades. For 2010–2019, it is found that the 1-year return level of daily maximum 8-h mean (MDA8) ozone exceeds the ‘moderate’ health threshold (100 µg/m3) at >90% of sites, but that the probability of extreme ozone events has markedly decreased since the 1980s. A machine learning methodology to downscale and bias correct a CTM (EMEP4UK) ozone surface was developed and evaluated. Compared to the unadjusted CTM, the downscaled surface exhibits a lower bias in reproducing MDA8 ozone allowing more robust assessments of important policy metrics. Analysis of the downscaled product (2014–2018) reveals on average 27% of the UK fails the government long-term objective for MDA8 ozone to not exceed 100 µg/m3 more than 10 times per year, compared to 99% in the unadjusted CTM. A classification-based machine learning analysis into high-level ozone drivers was also performed and shows a robust relationship between ozone and temperature. The method is demonstrated to offer remarkable promise as a tool with which to forecast the presence of high-level ozone. Despite a UK focus, the data-driven methods developed and applied here are applicable to modelling ozone in other regions of the world where measurements exist

Health-relevant, compound ozone and temperature burden in Europe: statistical modeling and climate change projections

Air pollution represents the major environmental health threat in Europe, with exposure to surface, ground-level ozone posing a major risk to the European population. Until the end of the 21st century, the health burden induced by ground-level ozone is expected to worsen due to ongoing climate change. Compound occurrences of health-relevant surface ozone and air temperature levels are of particular interest to environmental health science and projection studies, as there is evidence of an even intensified resulting health risk when both health stressors occur at the same time. The overall aim of the dissertation is to improve our current understanding and knowledge regarding recent and future health-relevant occurrences of ground-level ozone alone or alongside elevated levels of surface air temperature in Europe

Effects of 2010–2045 climate change on ozone levels in China under carbon neutrality scenario: Key meteorological parameters and processes

We examined the effects of 2010–2045 climate change on ozone (O3) levels in China under carbon neutrality scenario using the Global Change and Air Pollution version 2.0 (GCAP 2.0). In eastern China (EC), GCAP 2.0 and other six models from Coupled Model Intercomparison Projection Phase 6 (CMIP6) all projected increases in daily maximum 2-m temperature (T2max), surface incoming shortwave radiation (SW), and planet boundary layer height, and decreases in relative humidity (RH) and sea level pressure. Future climate change is simulated by GCAP 2.0 to have large effects on O3 even under carbon neutrality pathway, with summertime regional and seasonal mean MDA8 O3 concentrations increased by 2.3 ppbv (3.9 %) over EC, 4.7 ppbv (7.3 %) over North China Plain, and 3.0 ppbv (5.1 %) over Yangtze River Delta. Changes in key meteorological parameters were found to explain 58–76 % of the climate-driven MDA8 O3 changes over EC. The most important meteorological parameters in summer are T2max and SW in northern and central EC and RH in southern EC. Analysis showed net chemical production was the most important process that increases O3, accounting for 34.0–62.5 % of the sum of all processes within the boundary layer. We also quantified the uncertainties in climate-induced MDA8 O3 changes by using CMIP6 multi-model projections of climate and a stepwise multiple linear regression model. GCAP 2.0 results are in the lower-end of the climate-induced increases in MDA8 O3 from the multi-models. These results have important implications for policy-making regarding emission controls under the background of climate warming

State-of-art in modelling particulate matter (PM) concentration: a scoping review of aims and methods

Air pollution is the one of the most significant environmental risks to health worldwide. An accurate assessment of population exposure would require a continuous distribution of measuring ground-stations, which is not feasible. Therefore, significant efforts are spent in implementing air-quality models. However, a complex scenario emerges, with the spread of many different solutions, and a consequent struggle in comparison, evaluation and replication, hindering the definition of the state-of-art. Accordingly, aim of this scoping review was to analyze the latest scientific research on air-quality modelling, focusing on particulate matter, identifying the most widespread solutions and trying to compare them. The review was mainly focused, but not limited to, machine learning applications. An initial set of 940 results published in 2022 were returned by search engines, 142 of which resulted significant and were analyzed. Three main modelling scopes were identified: correlation analysis, interpolation and forecast. Most of the studies were relevant to east and southeast Asia. The majority of models were multivariate, including (besides ground stations) meteorological information, satellite data, land use and/or topography, and more. 232 different algorithms were tested across studies (either as single-blocks or within ensemble architectures), of which only 60 were tested more than once. A performance comparison showed stronger evidence towards the use of Random Forest modelling, in particular when included in ensemble architectures. However, it must be noticed that results varied significantly according to the experimental set-up, indicating that no overall best solution can be identified, and a case-specific assessment is necessary

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

Essays in Environmental Economics and Policy

This dissertation is concerned with the impacts of environmental stressors on people’s health and welfare. In particular, it focuses on air pollution and temperatures anomalies. Air pollution is considered the fifth leading mortality risk factor worldwide (Cohen et al., 2017). Despite impressive improvements in air quality over the last half-century, air pollution remains a global challenge, especially in rapidly urbanizing countries. On an even larger scale, anthropocentric emissions of greenhouse gases have been pushing a shift in the world’s climate that is projected to widen in the coming decades. The ability of economies to cope with changing temperatures is paramount to limiting the damages from climate change. The three chapters of this thesis should be read in the framework set by these challenges. In each chapter, a specific question is brought to the surface, and the road to address it is outlined. Chapter 1 investigates the negative effect of air pollution on physical ability. A large share of the world’s population is employed in manual labor. Yet, our understanding of the productivity cots of air pollution for physically intense work remains limited. The chapter identifies in track and field competitions a natural experiment where cognition plays a minor role. Combining half a million competition results with weather and air quality data, it estimates the change in physical performance induced by variations in air pollution. Chapter 2 considers the methodological tools available to estimate the causal change in air pollution concentrations following a reduction in emissions. It recognizes the challenges to identification posed by fluctuations and trends in atmospheric conditions and proposes a machine learning approach to address them. The chapter then applies this strategy to quantify the reduction in pollution and related health benefits induced by the COVID-19 lockdown of Lombardy, Italy, in spring 2020. This work is a joint effort with Lara Aleluia Reis (RFF-CMCC European Institute on Economics and the Environment), Valentina Bosetti (Bocconi University), and Massimo Tavoni (Politecnico di Milano). Chapter 3 is concerned with the persistence of the effects of temperature anomalies on economic growth. If an adverse temperature shock damages the determinants of economic growth, we can expect losses from climate change - a permanent shift in the mean temperature - to be cumulative over time and, therefore, very costly. Despite the primary importance of this question for modeling the climate-economy interactions, data constraints and data-hungry approaches have led to inconclusive answers. This chapter presents a new and more efficient method to test for the persistence of effects; using three different GDP datasets, evidence emerges that temperature effects are indeed persistent. This chapter has been the output of joint work with Bernardo A. Bastien-Olvera and Frances C. Moore of the University of California at Davis

Investigating the enhancement of air pollutant predictions and understanding air quality disparities across racial, ethnic, and economic lines at US public schools

2022 Spring.Includes bibliographical references.Ambient air pollution has significant health and economic impacts worldwide. Even in the most developed countries, monitoring networks often lack the spatiotemporal density to resolve air pollution gradients. Though air pollution affects the entire population, it can disproportionately affect the disadvantaged and vulnerable communities in society. Pollutants such as fine particulate matter (PM2.5), nitrogen oxides (NO and NO2), and ozone, which have a variety of anthropogenic and natural sources, have garnered substantial research attention over the last few decades. Over half the world and over 80% of Americans live in urban areas, and yet many cities only have one or several air quality monitors, which limits our ability to capture differences in exposure within cities and estimate the resulting health impacts. Improving sub-city air pollution estimates could improve epidemiological and health-impact studies in cities with heterogeneous distributions of PM2.5, providing a better understanding of communities at-risk to urban air pollution. Biomass burning is a source of PM2.5 air pollution that can impact both urban and rural areas, but quantifying the health impacts of PM2.5 from biomass burning can be even more difficult than from urban sources. Monitoring networks generally lack the spatial density needed to capture the heterogeneity of biomass burning smoke, especially near the source fires. Due to limitations of both urban and rural monitoring networks several techniques have been developed to supplement and enhance air pollution estimates. For example, satellite aerosol optical depth (AOD) can be used to fill spatial gaps in PM monitoring networks, but AOD can be disconnected from time-resolved surface-level PM in a multitude of ways, including the limited overpass times of most satellites, daytime-only measurements, cloud cover, surface reflectivity, and lack of vertical-profile information. Observations of smoke plume height (PH) may provide constraints on the vertical distribution of smoke and its impact on surface concentrations. Low-cost sensor networks have been rapidly expanding to provide higher density air pollution monitoring. Finally, both geophysical modeling, statistical techniques such as machine learning and data mining, and combinations of all of the aforementioned datasets have been increasingly used to enhance surface observations. In this dissertation, we explore several of these different data sources and techniques for estimating air pollution and determining community exposure concentrations. In the first chapter of this dissertation, we assess PH characteristics from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) and evaluate its correlation with co-located PM2.5 and AOD measurements. PH is generally highest over the western US. The ratio PM2.5:AOD generally decreases with increasing PH:PBLH (planetary boundary layer height), showing that PH has the potential to refine surface PM2.5 estimates for collections of smoke events. Next, to estimate spatiotemporal variability in PM2.5, we use machine learning (Random Forests; RFs) and concurrent PM2.5 and AOD measurements from the Citizen Enabled Aerosol Measurements for Satellites (CEAMS) low-cost sensor network as well as PM2.5 measurements from the Environmental Protection Agency's (EPA) reference monitors during wintertime in Denver, CO, USA. The RFs predicted PM2.5 in a 5-fold cross validation (CV) with relatively high skill (95% confidence interval R2=0.74-0.84 for CEAMS; R2=0.68-0.75 for EPA) though the models were aided by the spatiotemporal autocorrelation of the PM22.5 measurements. We find that the most important predictors of PM2.5 are factors associated with pooling of pollution in wintertime, such as low planetary boundary layer heights (PBLH), stagnant wind conditions, and, to a lesser degree, elevation. In general, spatial predictors are less important than spatiotemporal predictors because temporal variability exceeds spatial variability in our dataset. Finally, although concurrent AOD is an important predictor in our RF model for hourly PM2.5, it does not improve model performance during our campaign period in Denver. Regardless, we find that low-cost PM2.5 measurements incorporated into an RF model were useful in interpreting meteorological and geographic drivers of PM2.5 over wintertime Denver. We also explore how the RF model performance and interpretation changes based on different model configurations and data processing. Finally, we use high resolution PM2.5 and nitrogen dioxide (NO2) estimates to investigate socioeconomic disparities in air quality at public schools in the contiguous US. We find that Black and African American, Hispanic, and Asian or Pacific Islander students are more likely to attend schools in locations where the ambient concentrations of NO2 and PM2.5 are above the World Health Organization's (WHO) guidelines for annual-average air quality. Specifically, we find that ~95% of students that identified as Asian or Pacific Islander, 94% of students that identified as Hispanic, and 89% of students that identified as Black or African American, attended schools in locations where the 2019 ambient concentrations were above the WHO guideline for NO2 (10 μg m-3 or ~5.2 ppbv). Conversely, only 83% of students that identified as white and 82% of those that identified as Native American attended schools in 2019 where the ambient NO2 concentrations were above the WHO guideline. Similar disparities are found in annually averaged ambient PM2.5 across racial and ethnic groups, where students that identified as white (95%) and Native American (83%) had a smallest percentage of students above the WHO guideline (5 μg m-3), compared to students that identified with minoritized groups (98-99%). Furthermore, the disparity between white students and other minoritized groups, other than Native Americans, is larger at higher PM2.5 concentrations. Students that attend schools where a higher percentage of students are eligible for free or reduced meals, which we use as a proxy for poverty, are also more likely to attend schools where the ambient air pollutant concentrations exceed WHO guidelines. These disparities also tend to increase in magnitude at higher concentrations of NO2 and PM2.5. We investigate the intersectionality of disparities across racial/ethnic and poverty lines by quantifying the mean difference between the lowest and highest poverty schools, and the most and least white schools in each state, finding that most states have disparities above 1 ppbv of NO2 and 0.5 μg m-3 of PM2.5 across both. We also identify distinct regional patterns of disparities, highlighting differences between California, New York, and Florida. Finally, we also highlight that disparities do not only exist across an urban and non-urban divide, but also within urban areas