Abstract
Quantifying the coevolution of greenhouse gases and air quality pollutants can provide insight into underlying anthropogenic processes enabling predictions of their emission trajectories. ...Here, we classify the dynamics of historic emissions in terms of a modified Environmental Kuznets Curve (MEKC), which postulates the coevolution of fossil fuel CO
2
(FFCO
2
) and NOx emissions as a function of macroeconomic development. The MEKC broadly captures the historic FFCO
2
-NO
x
dynamical regimes for countries including the US, China, and India as well as IPCC scenarios. Given these dynamics, we find the predictive skill of FFCO2 given NO
x
emissions constrained by satellite data is less than 2% error at one-year lags for many countries and less than 10% for 4-year lags. The proposed framework in conjunction with an increasing satellite constellation provides valuable guidance to near-term emission scenario development and evaluation at time-scales relevant to international assessments such as the Global Stocktake.
Ground and satellite observations show that air pollution regulations in the United States (US) have resulted in substantial reductions in emissions and corresponding improvements in air quality over ...the last several decades. However, large uncertainties remain in evaluating how recent regulations affect different emission sectors and pollutant trends. Here we show a significant slowdown in decreasing US emissions of nitrogen oxides (NOₓ) and carbon monoxide (CO) for 2011–2015 using satellite and surface measurements. This observed slowdown in emission reductions is significantly different from the trend expected using US Environmental Protection Agency (EPA) bottom-up inventories and impedes compliance with local and federal agency air-quality goals. We find that the difference between observations and EPA’s NOₓ emission estimates could be explained by: (i) growing relative contributions of industrial, area, and off-road sources, (ii) decreasing relative contributions of on-road gasoline, and (iii) slower than expected decreases in on-road diesel emissions.
The COVID‐19 pandemic perturbed air pollutant emissions as cities shut down worldwide. Peroxyacyl nitrates (PANs) are important tracers of photochemistry that are formed through the oxidation of ...non‐methane volatile organic compounds in the presence of nitrogen oxide radicals (NOx = NO + NO2). We use satellite measurements of free tropospheric PANs from the Suomi‐National Polar‐orbiting Partnership Cross‐track Infrared Sounder (CrIS) over eight of the world's megacities. We quantify the seasonal cycle of PANs over these megacities and find seasonal maxima in PANs correspond to seasonal peaks in local photochemistry. CrIS is used to explore changes in PANs in response to the COVID‐19 lockdowns. Statistically significant changes to PANs occurred over four megacities: with decreases over Los Angeles and Delhi, and increases over Mexico City and Beijing in the winter. Our analysis suggests that large perturbations in NOx may not result in significant declines in NOx export potential of megacities.
Plain Language Summary
The COVID‐19 pandemic led to the lockdown of urban centers worldwide, drastically perturbing the concentrations of global air pollutants. Peroxyacyl nitrates (PANs) are important photochemical pollutants formed from reactions between NOx and volatile organic compounds, which were substantially reduced during the pandemic. We use satellite measurements of PANs from the Suomi‐National Polar‐orbiting Partnership Cross‐track Infrared Sounder in the free troposphere over and surrounding eight of the world's megacities. Seasonal cycles of PANs are pronounced and the seasonal maxima correspond to seasonal peaks in local photochemistry. Significant changes to PANs in response to COVID‐19 occurred over four out of the eight cities: PANs decreased over Los Angeles and Delhi, and PANs increased over Mexico City and Beijing in the winter. Our results indicate that large changes in NOx may not result in equally significant changes to PANs and the NOx export potential of megacities.
Key Points
There are pronounced seasonal cycles of peroxyacyl nitrates (PANs) over each megacity that align with seasonal maximums in photochemistry
Observed free tropospheric mixing ratios of PANs during COVID‐19 were significantly different over four out of eight surveyed megacities
Sensitivity of free tropospheric PANs to the abundance of precursors is seasonally dependent in some locations
Surface NOx emissions are estimated by a combined assimilation of satellite observations of NO2, CO, O3, and HNO3 with a global chemical transport model. The assimilation of measurements for species ...other than NO2 provides additional constraints on the NOx emissions by adjusting the concentrations of the species affecting the NOx chemistry and leads to changes in the regional monthly‐mean emissions of −58 to +32% and the annual total emissions of −16 to +3%. These large changes highlight that uncertainties in the model chemistry impact the quality of the emission estimates. In the inversion from NO2 observations only, NOx analysis increments occur closer to the surface. Because of the shorter residence time, larger emissions increments are required compared to the multiple species assimilation. Validation against independent observations and comparisons with the recent Regional Emission inventory in Asia version 2.1 emissions shows that the multiple species assimilation improves the chemical consistency including the relation between concentrations and the estimated emissions.
Key Points
Multi‐species satellite retrievals are assimilated
Non‐NO2 measurements place additional constraints on NOx emissions
Improved consistency of the concentrations and the estimated emissions
Data‐driven methods have been extensively applied to predict atmospheric compositions. Here, we explore the capability of a deep learning (DL) model to make ozone (O3) predictions across continents ...in China, the United States (US) and Europe. The DL model was trained and validated with surface O3 observations in China and the US in 2015–2018. The DL model was applied to predict hourly surface O3 over three continents in 2015–2022. Compared to baseline simulations using GEOS‐Chem (GC) model, our analysis exhibits mean biases of 2.6 and 4.8 μg/m3 with correlation coefficients of 0.94 and 0.93 (DL); and mean biases of 3.7 and 5.4 μg/m3 with correlation coefficients of 0.95 and 0.92 (GC) in Europe in 2015–2018 and 2019–2022, respectively. The comparable performances between DL and GC indicate the potential of DL to make reliable predictions over spatial and temporal domains where a wealth of local observations for training is not available.
Plain Language Summary
Machine learning techniques have been extensively applied in the field of atmospheric science. It provides an efficient way of integrating data and predicting atmospheric compositions. Here, we explore the capability of a deep learning (DL) model to make ozone (O3) predictions across continents in China, the United States (US) and Europe. The DL model was trained and validated with surface O3 observations in China and the US in 2015–2018. We then applied the DL model to predict surface O3 concentrations in China, the US and Europe in 2015–2022. Our analysis exhibits comparable performances between DL and chemical transport models for surface O3 predictions in Europe. This indicates the potential of DL models to extend predictions across spatial and temporal domains.
Key Points
Deep learning exhibits acceptable capabilities of spatial and temporal extrapolations for surface O3 predictions
Comparable performances between deep learning and GEOS‐Chem models with respect to independent O3 observations
Good performance of deep learning for rapid hourly O3 predictions
A data assimilation system for the analysis of atmospheric circulation and long‐lived tracer distributions in the troposphere and stratosphere has been developed and tested by using a local ensemble ...transform Kalman filter (LETKF), which has been applied to assimilate both meteorological fields and tracer concentrations into a general circulation model and an atmospheric transport model. Assimilated meteorological fields are used for driving the transport model. The performance of the LETKF data assimilation system is assessed under idealized conditions by assuming that the forecast models provide a perfect representation of atmospheric conditions. The LETKF meteorological analysis facilitates the study of atmospheric transport characteristics and provides high‐quality tracer transport simulations, reflecting its flow‐dependent and physically well balanced analysis. In particular, eddy mixing features are better analyzed by LETKF than by an analysis that employs a conventional assimilation scheme (i.e., nudging technique). The conventional analysis causes excessive tracer eddy dispersions, which were commonly observed in previous studies using three‐dimensional variational analyses. Further improvement in tracer analysis can be achieved by assimilating the tracer concentration within the LETKF. The assimilation of tracer concentration effectively reduces the tracer background error caused by initial distribution and surface flux errors. Tracer analysis can also be improved by considering the covariance with wind fields in a background error matrix, in which wind observation directly impacts the tracer states, reducing 20% of the tracer analysis error in the free troposphere. The sensitivity of the tracer analysis to assimilation parameters and model errors is discussed to obtain an optimal data assimilation framework.
Bottom-up emission inventories can provide valuable information for understanding emission status and are needed as input datasets to drive chemical transport models. However, this type of inventory ...has the disadvantage of taking several years to be compiled because it relies on a statistical dataset. Top-down approaches use satellite data as a constraint and overcome this disadvantage. We have developed an immediate inversion system to estimate anthropogenic NOx emissions with NO2 column density constrained by satellite observations. The proposed method allows quick emission updates and considers model and observation errors by applying linear unbiased optimum estimations. We used this inversion system to estimate the variation of anthropogenic NOx emissions from China and India from 2005 to 2016. On the one hand, NOx emissions from China increased, reaching a peak in 2011 with 29.5 Tg yr−1, and subsequently decreased to 25.2 Tg yr−1 in 2016. On the other hand, NOx emissions from India showed a continuous increase from 2005 to 2016, reaching 13.9 Tg yr−1 in 2016. These opposing trends from 2011 to 2016 were −0.83 and +0.76 Tg yr−1 over China and India, respectively, and correspond to strictly regulated and unregulated future scenarios. Assuming these trends continue after 2016, we expect NOx emissions from China and India will be similar in 2023, with India becoming the world's largest NOx emissions source in 2024.
PDF
