Air quality and health co-benefits of vehicle electrification and emission controls in the most populated United States urban hubs: Insights from New York, Los Angeles, Chicago, and Houston

Transitioning to electric vehicles (EVs) is a prominent strategy for reducing greenhouse gas emissions. However, given the complexity of atmospheric chemistry, the nuanced implications on air quality are yet to be fully understood. Our study delved into changes in PM2.5, ozone, and their associated precursors in major US urban areas, considering various electrification and mitigation scenarios. In the full electrification (FullE) scenario, PM2.5 reduction peaked at values between 0.34 and 2.29 μg.m-3 across distinct regions. Yet, certain areas in eastern Los Angeles exhibited a surprising uptick in PM2.5, reaching as much as 0.67 μg.m-3. This phenomenon was linked to a surge in secondary organic aerosols (SOAs), resulting from shifting NOx/VOCs (volatile organic compounds) dynamics and a spike in hydroxyl radical (OH) concentrations. The FullE scenario ushered in marked reductions in both NOx and maximum daily average 8-h (MDA8) ozone concentrations, with maximum levels ranging from 14.00 to 32.34 ppb and 2.58-9.58 ppb, respectively. However, certain instances revealed growths in MDA8 ozone concentrations, underscoring the intricacies of air quality management. From a health perspective, in the FullE scenario, New York, Chicago, and Houston stand to potentially avert 796, 328, and 157 premature deaths/month, respectively. Los Angeles could prevent 104 premature deaths/month in the HighE-BL scenario, representing a 29 % EV share for light-duty vehicles. However, the FullE scenario led to a rise in mortality in Los Angeles due to increased PM2.5 and MDA8 ozone levels. Economically, the FullE scenario projects health benefits amounting to 51-249 million $/day for New York, Chicago, and Houston. In contrast, Los Angeles may face economic downturns of up to 18 million $/day. In conclusion, while EV integration has the potential to improve urban air quality, offering substantial health and economic advantages, challenges persist. Our results emphasize the pivotal role of VOCs management, providing policymakers with insights for adaptable and efficient measures.

Contributions of meteorology to ozone variations: Application of deep learning and the Kolmogorov-Zurbenko filter

From hourly ozone observations obtained from three regions⸻Houston, Dallas, and West Texas⸻we investigated the contributions of meteorology to changes in surface daily maximum 8-h average (MDA8) ozone from 2000 to 2019. We applied a deep convolutional neural network and Shapely additive explanation (SHAP) to examine the complex underlying nonlinearity between variations of surface ozone and meteorological factors. Results of the models showed that between 2000 and 2019, specific humidity (38% and 27%) and temperature (28% and 37%) contributed the most to ozone formation over the Houston and Dallas metropolitan areas, respectively. On the other hand, the results show that solar radiation (50%) strongly impacted ozone variation over West Texas during this time. Using a combination of the Kolmogorov-Zurbenko (KZ) filter and multiple linear regression, we also evaluated the influence of meteorology on ozone and quantified the contributions of meteorological parameters to trends in surface ozone formation. Our findings showed that in Houston and Dallas, meteorology influenced ozone variations to a large extent. The impacts of meteorology on West Texas, however, showed meteorological factors had fewer influences on ozone variabilities from 2000 to 2019. This study showed that SHAP analysis and the KZ approach can investigate the contributions of the meteorological factors on ozone concentrations and help policymakers enact effective ozone mitigation policies.

The sensitivities of ozone and PM2.5 concentrations to the satellite-derived leaf area index over East Asia and its neighboring seas in the WRF-CMAQ modeling system

Vegetation plays an important role as both a sink of air pollutants via dry deposition and a source of biogenic VOC (BVOC) emissions which often provide the precursors of air pollutants. To identify the vegetation-driven offset between the deposition and formation of air pollutants, this study examines the responses of ozone and PM_2.5 concentrations to changes in the leaf area index (LAI) over East Asia and its neighboring seas, using up-to-date satellite-derived LAI and green vegetation fraction (GVF) products. Two LAI scenarios that examine (1) table-prescribed LAI and GVF from 1992 to 1993 AVHRR and 2001 MODIS products and (2) reprocessed 2019 MODIS LAI and 2019 VIIRS GVF products were used in WRF-CMAQ modeling to simulate ozone and PM_2.5 concentrations for June 2019. The use of up-to-date LAI and GVF products resulted in monthly mean LAI differences ranging from -56.20% to 96.81% over the study domain. The increase in LAI resulted in the differences in hourly mean ozone and PM_2.5 concentrations over inland areas ranging from 0.27 ppbV to -7.17 ppbV and 0.89 μg/m³ to -2.65 μg/m³, and the differences of those over the adjacent sea surface ranging from 0.69 ppbV to -2.86 ppbV and 3.41 μg/m³ to -7.47 μg/m³. The decreases in inland ozone and PM_2.5 concentrations were mainly the results of dry deposition accelerated by increases in LAI, which outweighed the ozone and PM_2.5 formations via BVOC-driven chemistry. Some inland regions showed further decreases in PM_2.5 concentrations due to reduced reactions of PM_2.5 precursors with hydroxyl radicals depleted by BVOCs. The reductions in sea surface ozone and PM_2.5 concentrations were accompanied by the reductions in those in upwind inland regions, which led to less ozone and PM_2.5 inflows. The results suggest the importance of the selective use of vegetation parameters for air quality modeling.

Changes in the ozone chemical regime over the contiguous United States inferred by the inversion of NOx and VOC emissions using satellite observation

To investigate changes in the ozone (O₃) chemical production regime over the contiguous United States (CONUS) with accurate knowledge of concentrations of its precursors, we applied an inverse modeling technique with Ozone Monitoring Instrument (OMI) tropospheric nitrogen dioxide (NO₂) and total formaldehyde (HCHO) retrieval products in the summers of 2011, 2014, and 2017, years in which United States National Emission Inventory were based. The inclusion of dynamic chemical lateral boundary conditions and lightning-induced nitric oxide emissions significantly account for the contribution of background sources in the free troposphere. Satellite-constrained nitrogen oxide (NO_x) and non-methane volatile organic compounds (NMVOCs) emissions mitigate the discrepancy between satellite and modeled columns: the inversion suggested 2.33-2.84 (1.07-1.34) times higher NO_x over the CONUS (over urban regions) and 0.28-0.81 times fewer NMVOCs emissions over the southeastern United States. The model-derived HCHO/NO₂ column ratio shows gradual spatial changes in the O₃ production regime near urban cores relative to previously defined threshold values representing NO_x and VOC sensitive conditions. We also found apparent shifts from the NO_x-saturated regime to the transition regime (or the transition regime to the NO_x-limited regime) over the major cities in the western United States. In contrast, rural areas, especially in the east-southeastern United States, exhibit a decreased HCHO/NO₂ column ratio by -1.30 ± 1.71 with a reduction in HCHO column primarily driven by meteorology, becoming sensitive to VOC emissions. Results show that incorporating satellite observations into numerical modeling could help policymakers implement appropriate emission control policies for O₃ pollution.

A comprehensive study of the COVID-19 impact on PM2.5 levels over the contiguous United States: A deep learning approach

We investigate the impact of the COVID-19 outbreak on PM_2.5 levels in eleven urban environments across the United States: Washington DC, New York, Boston, Chicago, Los Angeles, Houston, Dallas, Philadelphia, Detroit, Phoenix, and Seattle. We estimate daily PM_2.5 levels over the contiguous U.S. in March-May 2019 and 2020, and leveraging a deep convolutional neural network, we find a correlation coefficient, an index of agreement, a mean absolute bias, and a root mean square error of 0.90 (0.90), 0.95 (0.95), 1.34 (1.24) μg/m³, and 2.04 (1.87) μg/m³, respectively. Results from Google Community Mobility Reports and estimated PM_2.5 concentrations show a greater reduction of PM_2.5 in regions with larger decreases in human mobility and those in which individuals remain in their residential areas longer. The relationship between vehicular PM_2.5 (i.e., the ratio of vehicular PM_2.5 to other sources of PM_2.5) emissions and PM_2.5 reductions (R = 0.77) in various regions indicates that regions with higher emissions of vehicular PM_2.5 generally experience greater decreases in PM_2.5. While most of the urban environments ⸺ Washington DC, New York, Boston, Chicago, Los Angeles, Houston, Dallas, Philadelphia, Detroit, and Seattle ⸺ show a decrease in PM_2.5 levels by 21.1%, 20.7%, 18.5%, 8.05%, 3.29%, 3.63%, 6.71%, 4.82%, 13.5%, and 7.73%, respectively, between March-May of 2020 and 2019, Phoenix shows a 5.5% increase during the same period. Similar to their PM_2.5 reductions, Washington DC, New York, and Boston, compared to other cities, exhibit the highest reductions in human mobility and the highest vehicular PM_2.5 emissions, highlighting the great impact of human activity on PM_2.5 changes in eleven regions. Moreover, compared to changes in meteorological factors, changes in pollutant concentrations, including those of black carbon, organic carbon, SO₂, SO₄, and especially NO₂, appear to have had a significantly greater impact on PM_2.5 changes during the study period.

Impact of the COVID-19 outbreak on air pollution levels in East Asia

This study leverages satellite remote sensing to investigate the impact of the coronavirus outbreak and the resulting lockdown of public venues on air pollution levels in East Asia. We analyze data from the Sentinel-5P and the Himawari-8 satellites to examine concentrations of NO₂, HCHO, SO₂, and CO, and the aerosol optical depth (AOD) over the BTH, Wuhan, Seoul, and Tokyo regions in February 2019 and February 2020. Results show that most of the concentrations of pollutants are lower than those of February 2019. Compared to other pollutants, NO₂ experienced the most significant reductions by almost 54%, 83%, 33%, and 19% decrease in BTH, Wuhan, Seoul, and Tokyo, respectively. The greatest reductions in pollutants occurred in Wuhan, with a decrease of almost 83%, 11%, 71%, and 4% in the column densities of NO₂, HCHO, SO₂, and CO, respectively, and a decrease of about 62% in the AOD. Although NO₂, CO, and formaldehyde concentrations decreased in the Seoul and Tokyo metropolitan areas compared to the previous year, concentrations of SO₂ showed an increase in these two regions due to the effect of transport from polluted upwind regions. We also show that meteorological factors were not the main reason for the dramatic reductions of pollutants in the atmosphere. Moreover, an investigation of the HCHO/NO₂ ratio shows that in many regions of East China, particularly in Wuhan, ozone production in February 2020 is less NO_X saturated during the daytime than it was in February 2019. With large reductions in the concentrations of NO₂ during lockdown situations, we find that significant increases in surface ozone in East China from February 2019 to February 2020 are likely the result of less reaction of NO and O₃ caused by significantly reduced NO_X concentrations and less NO_X saturation in East China during the daytime.