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.

A comprehensive approach combining positive matrix factorization modeling, meteorology, and machine learning for source apportionment of surface ozone precursors: Underlying factors contributing to ozone formation in Houston, Texas.

Ozone concentrations in Houston, Texas, are among the highest in the United States, posing significant risks to human health. This study aimed to evaluate the impact of various emissions sources and meteorological factors on ozone formation in Houston from 2017 to 2021 using a comprehensive PMF-SHAP approach. First, we distinguished the unique sources of VOCs in each area and identified differences in the local chemistry that affect ozone production. At the urban station, the primary sources were n_decane, biogenic/industrial/fuel evaporation, oil and gas flaring/production, industrial emissions/evaporation, and ethylene/propylene/aromatics. At the industrial site, the main sources were industrial emissions/evaporation, fuel evaporation, vehicle-related sources, oil and gas flaring/production, biogenic, aromatic, and ethylene and propylene. And then, we performed SHAP analysis to determine the importance and impact of each emissions factor and meteorological variables. Shortwave radiation (SHAP values are ∼5.74 and ∼6.3 for Milby Park and Lynchburg, respectively) and humidity (∼4.87 and ∼4.71, respectively) were the most important variables for both sites. For the urban station, the most important emissions sources were n_decane (∼2.96), industrial emissions/evaporation (∼1.89), and ethylene/propylene/aromatics (∼1.57), while for the industrial site, they were oil and gas flaring/production (∼1.38), ethylene/propylene (∼1.26), and industrial emissions/evaporation (∼0.95). NOx had a negative impact on ozone production at the urban station due to the NOx-rich chemical regime, whereas NOx had positive impacts at the industrial site. The study's findings suggest that the PMF-SHAP approach is efficient, inexpensive, and can be applied to other similar applications to identify factors contributing to ozone-exceedance events. The study's results can be used to develop more effective air quality management strategies for Houston and other cities with high levels of ozone.

Deep learning mapping of surface MDA8 ozone: The impact of predictor variables on ozone levels over the contiguous United States.

The limited number of ozone monitoring stations imposes uncertainty in various applications, calling for accurate approaches to capturing ozone values in all regions, particularly those with no in-situ measurements. This study uses deep learning (DL) to accurately estimate daily maximum 8-hr average (MDA8) ozone and examines the spatial contribution of several factors on ozone levels over the contiguous U.S. (CONUS) in 2019. A comparison between in-situ observations and DL-estimated MDA8 ozone values shows a correlation coefficient (R) of 0.95, an index of agreement (IOA) of 0.97, and a mean absolute bias (MAB) of 2.79 ppb, highlighting the promising performance of the deep convolutional neural network (Deep-CNN) at estimating surface MDA8 ozone. Spatial cross-validation also confirms the high spatial accuracy of the model, which obtains an R of 0.91, and IOA of 0.96 and an MAB of 3.46 ppb when it is trained and tested on separate stations. To interpret the black-box nature of our DL model, we use Shapley additive explanations (SHAP) to generate a spatial feature contribution map (SFCM), the results of which confirm an advanced ability of Deep-CNN to capture the interactions between most predictor variables and ozone. For instance, the model shows that solar radiation (SRad) SFCM, with higher values, enhances the formation of ozone, particularly in the south and southwestern CONUS. As SRad triggers ozone precursors to produce ozone via photochemical reactions, it increases ozone concentrations. The model also shows that humidity, with its low values, increases ozone concentrations in the western mountainous regions. The negative correlation between humidity and ozone levels can be attributed to factors such as higher ozone decomposition resulting from increased levels of humidity and OH radicals. This study is the first to introduce the SFCM to investigate the spatial role of predictor variables on changes in estimated MDA8 ozone levels.

Current Status and Future Forecast of Short-lived Climate-Forced Ozone in Tehran, Iran, derived from Ground-Based and Satellite Observations.

In this study, the distribution and alterations of ozone concentrations in Tehran, Iran, in 2021 were investigated. The impacts of precursors (i.e., CO, NO2, and NO) on ozone were examined using the data collected over 12 months (i.e., January 2021 to December 2021) from 21 stations of the Air Quality Control Company (AQCC). The results of monthly heat mapping of tropospheric ozone concentrations indicated the lowest value in December and the highest value in July. The lowest and highest seasonal concentrations were in winter and summer, respectively. Moreover, there was a negative correlation between ozone and its precursors. The Inverse Distance Weighting (IDW) method was then implemented to obtain air pollution zoning maps. Then, ozone concentration modeled by the IDW method was compared with the average monthly change of total column density of ozone derived from Sentinel-5 satellite data in the Google Earth Engine (GEE) cloud platform. A good agreement was discovered despite the harsh circumstances that both ground-based and satellite measurements were subjected to. The results obtained from both datasets showed that the west of the city of Tehran had the highest averaged O3 concentration. In this study, the status of the concentration of ozone precursors and tropospheric ozone in 2022 was also predicted. For this purpose, the Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) approach was implemented to predict the monthly air quality parameters. Overall, it was observed that the SARIMA approach was an efficient tool for forecasting air quality. Finally, the results showed that the trends of ozone obtained from terrestrial and satellite observations throughout 2021 were slightly different due to the contribution of the tropospheric ozone precursor concentration and meteorology conditions.

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.

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 (NOx) 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 NOx 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 NOx and VOC sensitive conditions. We also found apparent shifts from the NOx-saturated regime to the transition regime (or the transition regime to the NOx-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 PM2.5 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 PM2.5 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 PM2.5 concentrations show a greater reduction of PM2.5 in regions with larger decreases in human mobility and those in which individuals remain in their residential areas longer. The relationship between vehicular PM2.5 (i.e., the ratio of vehicular PM2.5 to other sources of PM2.5) emissions and PM2.5 reductions (R = 0.77) in various regions indicates that regions with higher emissions of vehicular PM2.5 generally experience greater decreases in PM2.5. While most of the urban environments ⸺ Washington DC, New York, Boston, Chicago, Los Angeles, Houston, Dallas, Philadelphia, Detroit, and Seattle ⸺ show a decrease in PM2.5 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 PM2.5 reductions, Washington DC, New York, and Boston, compared to other cities, exhibit the highest reductions in human mobility and the highest vehicular PM2.5 emissions, highlighting the great impact of human activity on PM2.5 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 PM2.5 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 NOX 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 NOX concentrations and less NOX saturation in East China during the daytime.