A retrospect of ozone formation mechanisms during the COVID-19 lockdown: The potential role of isoprene.

Wuhan took strict measures to prevent the spread of COVID-19 from January 26 to April 7 in 2020. The lockdown reduced the concentrations of atmospheric pollutants, except ozone (O3). To investigate the increase in O3 during the lockdown, trace gas pollutants were collected. The initial concentrations of volatile organic compounds (VOCs) were calculated based on a photochemical ratio method, and the ozone formation potential (OFP) was obtained using the initial and measured VOC concentrations. The O3 formation regime was NOX-limited based on the VOCs/NOX diurnal ratios during the lockdown period. The reduced nitric oxide (NO) concentrations and lower wind speed (WS) could explain the night-time O3 accumulation. The initial total VOCs (TVOCs) during the lockdown were 47.6 ± 2.9 ppbv, and alkenes contributed 48.1%. The photochemical loss amounts of alkenes were an order of magnitude higher than those of alkenes in the same period in 2019 and increased from 16.6 to 28.0 ppbv in the daytime. The higher initial alkene concentrations sustained higher OFP during the lockdown, reaching between 252.4 and 504.4 ppbv. The initial isoprene contributed approximately 35.0-55.0% to the total OFP and had a positive correlation with the increasing O3 concentrations. Approximately 75.5% of the temperatures were concentrated in the range of 5 and 20 °C, which were higher than those in 2019. In addition to stronger solar radiation, the higher temperatures induced higher isoprene emission rates, partially accounting for the higher isoprene concentrations. Lower isoprene-emitting trees should be considered for future urban vegetation to control O3 episodes.

Changes in tropospheric ozone concentration over Indo-Gangetic Plains: the role of meteorological parameters

This study seeks to understand and quantify the changes in tropospheric ozone (O3) in lower troposphere (LT), middle troposphere (MT) and upper middle troposphere (UMT) over the Indo-Gangetic Plains (IGPs), India during the COVID-19 lockdown 2020 with that of pre-lockdown 2019. The gridded datasets of ozone from the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis product, ERA5 in combination with statistical interpolated (IDWs) surface NO2 observations, present a consistent picture and indicate a significant tropospheric ozone enhancement over IGP during COVID-19 lockdown restrictions in May 2020. The Paper also examines the influencing role of meteorological parameters on increasing ozone concentration. Over LT, an increase in O3 concentration (23%) is observed and in MT to UMT an enhancement of about 9–18% in O3 concentration have been seen during May 2020 with respect to May 2019. An investigation on causes of increasing  ozone concentration (35–85 ppbv) from MT to UMT during May 2020 reveals that there was significant rise (by 1–6%) in low cloud cover (LCC). Notably, higher LCC increases the backscattering of upward solar radiation from the top of the atmosphere. A positive difference of 5–25 W/m2 in upward solar radiation (USR) is observed across the entire study region. The result suggests that higher LCC significantly contributed to the enhanced USR. Thereby, resulting in higher photolysis rate that lead to an increase in mid tropospheric ozone concentration during May 2020. The results highlight the importance of LCC as an important pathway in ozone formation and aid in scientific understanding of it.

Evaluating NOx emissions and their effect on O3 production in Texas using TROPOMI NO2 and HCHO

The Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite is a valuable source of information to monitor the NOx emissions that adversely affect air quality. We conduct a series of experiments using a 4×4 km2 Comprehensive Air Quality Model with Extensions (CAMx) simulation during April–September 2019 in eastern Texas to evaluate the multiple challenges that arise from reconciling the NOx emissions in model simulations with TROPOMI. We find an increase in NO2 (+17 % in urban areas) when transitioning from the TROPOMI NO2 version 1.3 algorithm to the version 2.3.1 algorithm in eastern Texas, with the greatest difference (+25 %) in the city centers and smaller differences (+5 %) in less polluted areas. We find that lightningNOx emissions in the model simulation contribute up to 24 % of the column NO2 in the areas over the Gulf of Mexico and 8% in Texas urban areas. NOx emissions inventories, when using locally resolved inputs, agree with NOx emissions derived from TROPOMI NO2 version 2.3.1 to within 20 % in most circumstances, with a small NOx underestimate in Dallas–Fort Worth (-13 %) and Houston (-20 %). In the vicinity of large power plant plumes (e.g., Martin Lake and Limestone) we find larger disagreements, i.e., the satellite NO2 is consistently smaller by 40 %–60 % than the modeled NO2, which incorporates measured stack emissions. We find that TROPOMI is having difficulty distinguishingNO2 attributed to power plants from the background NO2 concentrations in Texas – an area with atmospheric conditions that cause short NO2 lifetimes. Second, the NOx/NO2 ratio in the model may be underestimated due to the 4 km grid cell size. To understand ozone formation regimes in the area, we combine NO2 column information with formaldehyde (HCHO) column information. We find modest low biases in the model relative to TROPOMI HCHO, with -9 % underestimate in eastern Texas and -21 % in areas of central Texas with lower biogenic volatile organic compound (VOC) emissions. Ozone formation regimes at the time of the early afternoon overpass are NOx limited almost everywhere in the domain, except along the Houston Ship Channel, near the Dallas/Fort Worth International airport, and in the presence of undiluted power plant plumes. There are likely NOx-saturated ozone formation conditions in the early morning hours that TROPOMI cannot observe and would be well-suited for analysis with NO2 and HCHO from the upcoming TEMPO (Tropospheric Emissions: Monitoring Pollution) mission. This study highlights that TROPOMI measurements offer a valuable means to validate emissions inventories and ozone formation regimes, with important limitations.

Ground-level ozone pollution in China: a synthesis of recent findings on influencing factors and impacts

Ozone (O3) in the troposphere is an air pollutant and a greenhouse gas. In mainland China, after the Air Pollution Prevention and Action Plan was implemented in 2013—and despite substantial decreases in the concentrations of other air pollutants—ambient O3 concentrations paradoxically increased in many urban areas. The worsening urban O3 pollution has fuelled numerous studies in recent years, which have enriched knowledge about O3-related processes and their impacts. In this article, we synthesise the key findings of over 500 articles on O3 over mainland China that were published in the past six years in English-language journals. We focus on recent changes in O3 concentrations, their meteorological and chemical drivers, complex O3 responses to the drastic decrease in human activities during coronavirus disease 2019 lockdowns, several emerging chemical processes, impacts on crops and trees, and the latest government interventions.

[Characteristics, Ozone Formation Potential, and Source Apportionment of VOCs During the COVID-19 Epidemic in Xiong'an].

Many restrictive measures were implemented in China from January-February 2020 to control the rapid spread of COVID-19. Many studies reported that the COVID-19 lockdown impacted PM2.5, SO2, volatile organic compounds (VOCs), etc. VOCs play important roles in the production of ozone and PM2.5. Ambient VOCs in Xiong'an were measured from December 25, 2019 to January 24, 2020 (prior to epidemic prevention, P1) and from January 25, 2020 to February 24, 2020 (during epidemic prevention, P2) through a VOCs online instrument. In the study, VOCs characteristics and ozone generation potential (OFP) of ambient VOCs were analyzed, and source apportionment of VOCs were analyzed by using Positive Matrix Factorization (PMF). The results showed that φ(TVOCs) during epidemic prevention and control was 45.1×10-9, which was approximately half of that before epidemic prevention and control (90.5×10-9). The chemical composition of VOCs showed significant changes after epidemic prevention and control, the contribution rate of alkanes increased from 37.6% to 53.8%, and the contribution rate of aromatic hydrocarbons and halogenated hydrocarbons decreased from 13.3% and 12.0% to 7.5% and 7.8%, respectively. Aromatic hydrocarbons, halogenated hydrocarbons, and OVOCs decreased by more than 60%. Seven types of the top ten species were the same before and during the epidemic prevention and control, mainly low-carbon alkanes, olefins, aldehydes, and ketones. Dichloromethane, trichloromethane, and BTEXs decreased significantly. The OPP was 566 µg·m-3 and 231 µg·m-3 in P1 and P2, respectively. The OPP of VOCs decreased by more than 30%. The proportion of OFP contribution of aromatic hydrocarbons decreased significantly after the epidemic prevention and control, and the proportion of OFP contribution of alkanes and alkynes increased significantly. Positive matrix factorization (PMF) was then applied for VOCs sources apportionment. Six sources were identified, including background sources, oil-gas volatile sources, combustion sources, industrial sources, solvent use sources, and vehicle exhaust sources. The results showed that after the epidemic prevention and control, the contribution rate of solvent use sources to TVOCs decreased from 24% to 9%. The contribution rates of background sources, oil-gas volatile sources, and combustion sources increased from 13%, 34%, and 24% to 6%, 14%, and 13%, respectively. The relative contributions of vehicle exhaust sources before and after epidemic prevention and control were 21% and 18%, respectively. The observation points were affected by the emission of VOCs from paroxysmal industrial sources before the epidemic prevention and control. The emission was stopped after the epidemic prevention and control, and its contribution rate was reduced from 22% before the epidemic prevention and control to 1%. The concentrations of industrial sources, solvent sources, motor vehicle tail gas sources, and combustion sources decreased by 97%, 82%, 61%, and 15%, respectively, after the epidemic prevention and control. The concentration of background sources remained stable, and the concentration of oil and gas volatile sources increased by 7%. The control of production and traffic activities cannot reduce the emission of VOCs from oil and gas volatile sources, which is the focus of VOCs control in Xiong'an.

[Variation Characteristics of Ambient Volatile Organic Compounds (VOCs) Volume Fraction During Hangzhou COVID-19 Period].

A continuous observation campaign was carried out with the Syntech Spectras GC955 volatile organics online monitoring system from December 1, 2019 to March 31, 2020 during the COVID-19 period in Hangzhou. Composition characteristics, diurnal variation, and atmospheric chemical reactivity of VOCs were analyzed. The results showed that φ(total VOCs) were the highest before the COVID-19 pandemic in different sites and the lowest during the first response period. The φ(total VOCs) at night was higher than that during the day. The daily variation in Wolongqiao φ(total VOCs) was less than that in Xiasha. The daily variation in φ(total VOCs) during the first level response period was less than that during the other three periods. The diurnal variation in the φ (total VOCs) in Xiasha showed a "V" shape, and that in Wolongqiao showed a typical bimodal structure. The OFP in Xiasha was higher than that in Wolongqiao. The OFP were the highest at the two sites before the COVID-19 pandemic. The OFP was the lowest during the first response period in Xiasha and the lowest during the second response period in Wolongqiao. The OFP of aromatics and olefins was higher, and the OFP of alkynes was the lowest in Xiasha. The OFP of olefin in Wolongqiao was much higher than that of the other three components, followed by alkane and alkyne.