Cities in South and Southeast Asia are developing rapidly without routine, up-to-date knowledge of air pollutant precursor emissions. This data deficit can potentially be addressed for nitrogen oxides (NOx) by deriving city NOx emissions from satellite observations of nitrogen dioxide (NO2) sampled under windy conditions. NO2 plumes of isolated cities are aligned along a consistent wind-rotated direction and a best-fit Gaussian is applied to estimate emissions. This approach currently relies on non-standardized selection of the area to sample around the city centre and Gaussian fits often fail or yield non-physical parameters. Here, we automate this approach by defining many (54) sampling areas that we test with TROPOspheric Monitoring Instrument (TROPOMI) NO2 observations for 2019 over 19 cities in South and Southeast Asia. Our approach is efficient, adaptable to many cities, standardizes and eliminates sensitivity of the Gaussian fit to sampling area choice, and increases success of deriving annual emissions from 40-60% with one sampling area to 100% (all 19 cities) with 54. The annual emissions we estimate range from 16±5 mol s-1 for Yangon (Myanmar) and Bangalore (India) to 125±41 mol s-1 for Dhaka (Bangladesh). With the enhanced success of our approach, we find evidence from comparison of our top-down emissions to past studies and to inventory estimates that the wind rotation and EMG fit approach may be biased, as it does not adequately account for spatial and seasonal variability in NOx photochemistry. Further methodological development is needed to enhance its accuracy and to exploit it to derive sub-annual emissions.

Manganese (Mn) is a key cofactor in enzymes responsible for lignin decay (mainly Mn peroxidase), regulating the rate of litter degradation and carbon (C) turnover in temperate and boreal forest biomes.While soil Mn is mainly derived from bedrock, atmospheric Mn could also contribute to soil Mn cycling, especially within the surficial horizon, with implications for soil C cycling. However, quantification of the atmospheric Mn cycle, which comprises emissions from natural (desert dust, sea salts, volcanoes, primary biogenic particles, and wildfires) and anthropogenic sources (e.g. industrialization and land-use change due to agriculture) transport, and deposition into the terrestrial and marine ecosystem, remains uncertain. Here, we use compiled emission datasets for each identified source to model and quantify the atmospheric Mn cycle with observational constraints. We estimated global emissions of atmospheric Mn in aerosols (<10 µm in aerodynamic diameter) to be 1500 Gg Mn yr-1. Approximately 32% of the emissions come from anthropogenic sources. Deposition of the anthropogenic Mn shortened soil Mn “pseudo” turnover times in surficial soils about 1-m depth (ranging from 1,000 to over 10,000,000 years) by 1-2 orders of magnitude in industrialized regions. Such anthropogenic Mn inputs boosted the Mn-to-N ratio of the atmospheric deposition in non-desert dominated regions (between 5×10-5 and 0.02) across industrialized areas, but still lower than soil Mn-to-N ratio by 1-3 orders of magnitude. Correlation analysis revealed a negative relationship between Mn deposition and topsoil C density across temperate and (sub)tropical forests, illuminating the role of Mn deposition in these ecosystems.

Integrating air quality information from models, satellites, and in-situ monitors allows for both better estimation of air quality and better quantification of uncertainties in this estimation. Uncertainty quantification is important to appropriately convey confidence in these estimates and forecasts to users who will base decisions on these. Uncertainty quantification also allows tracing the value of information provided by different data sources. This can identify gaps in the monitoring network where additional data could further reduce uncertainties. This paper presents a framework for data fusion with uncertainty quantification, applicable to multiple air-quality-relevant pollutants. Testing of this framework in the context of nitrogen dioxide forecasting at sub-city scales shows promising results, with confidence intervals typically encompassing the expected number of actual measurements during cross-validation. The framework is now being implemented into an online tool to support local air quality management decision-making. Future work will also include the incorporation of low-cost air sensor data and the quantification of uncertainty at hyper-local scales.

A document by Olivia Sablan. Click on the document to view its contents.

The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheric composition. The resulting reductions in anthropogenic activity represent an unprecedented event that yields a glimpse into a future where emissions to the atmosphere are reduced. While air pollutants and greenhouse gases share many common anthropogenic sources, there is a sharp difference in the response of their atmospheric concentrations to COVID-19 emissions changes due in large part to their different lifetimes. Here, we discuss two key takeaways from modeling and observational studies. First, despite dramatic declines in mobility and associated vehicular emissions, the atmospheric growth rates of greenhouse gases were not slowed. Second, it demonstrated empirically that the response of atmospheric composition to emissions changes is heavily modulated by factors including carbon cycle feedbacks to CH4 and CO2, background pollutant levels, the timing and location of emissions changes, and climate feedbacks on air quality.

Air pollution levels are uneven within cities, contributing to persistent health disparities between neighborhoods and population sub-groups. Highly spatially resolved information on pollution levels and disease rates is necessary to characterize inequities in air pollution exposure and related health We leverage recent advances in deriving surface pollution levels from satellite remote sensing and granular data in disease rates for one city, Washington, DC, to assess intra-urban heterogeneity in fine particulate matter (PM5)- attributable mortality and We estimate PM2.5-attributable cases of all-cause mortality, chronic obstructive pulmonary disease, ischaemic heart disease, lung cancer, stroke, and asthma emergency department (ED) visits using epidemiologically-derived health impact Data inputs include satellite-derived annual mean surface PM5 concentrations; age-resolved population estimates; and statistical neighborhood-, zip code- and ward-scale disease counts. We find that PM5 concentrations and associated health burdens have decreased in DC between 2000 and 2018, from approximately 240 to 120 cause-specific deaths and from 40 to 30 asthma ED visits per year (between 2014 and 2018). However, remaining PM5-attributable health risks are unevenly and inequitably distributed across the Higher PM2.5-attributable disease burdens were found in neighborhoods with larger proportions of people of color, lower household income, and lower educational Our study adds to the growing body of literature documenting the inequity in air pollution exposure levels and pollution health risks between population sub-groups, and highlights the need for both high-resolution disease rates and concentration estimates for understanding intra-urban disparities in air pollution-related health risks.

Ambient nitrogen dioxide (NO2) and fine particulate matter (PM2.5) pollution threaten public health in the United States (U.S.), and systemic racism has led to modern-day disparities in the distribution and associated health impacts of these pollutants. Many studies on environmental injustices related to ambient air pollution focus only on disparities in pollutant concentrations or provide only an assessment of pollution or health disparities at a snapshot in time. In this study we aim to document changing disparities in pollution-attributable health burdens over time and, for the first time, disparities in NO2-attributable health impacts across the entire U.S. We show that, despite overall decreases in the public health damages associated with NO2 and PM2.5, ethnoracial relative disparities in NO2-attributable pediatric asthma and PM2.5-attributable premature mortality in the U.S. have widened during the last decade. Racial disparities in PM2.5 attributable premature mortality and NO2-attributable pediatric asthma have increased by 19% and 16%, respectively, between 2010 and 2019. Similarly, ethnic disparities in PM2.5-attributable premature mortality have increased by 40% and NO2-attributable pediatric asthma by 10%. These widening trends in air pollution disparities are reversed when more stringent air quality standard levels are met for both pollutants. Our methods provide a semi-observational approach to tracking changes in disparities in air pollution and associated health burdens across the U.S.