The volatilities of ambient organic aerosol (OA) components are important to forecasting OA formation with models. However, providing the OA volatility distribution inputs for models is challenging, and models often rely on measurements from chamber experiments. We measured the volatility of submicron ambient OA in Seoul during May/June of 2019 by connecting a thermodenuder to an Aerodyne Time-of-Flight Aerosol Mass Spectrometer (AMS). We calculated a volatility basis set (VBS) of the organic aerosol with a thermodenuder mass transfer model and data from the thermodenuder set to various temperatures (30–200 °C). We found a large discrepancy between the measured ambient VBS and a reference VBS used in air quality models, with the ambient organics being less volatile. The results suggest that a modeling study that tries to account for this discrepancy may be needed to identify the impact it has on modeling outcomes. Chamber experiments aiming to determine VBSs for specific chemical systems should address limitations caused by wall losses and incomplete modeling parameters.

Ground-based Multi-Axis Differential Optical Absorption Spectroscopy (Max-DOAS) measurements of nitrogen dioxide (NO₂) were continuously obtained from January to November 2019 in northeastern China (NEC). Seasonal variations in the mean NO₂ vertical column densities (VCDs) were apparent, with a maximum of 2.9 × 10¹⁶ molecules cm⁻² in the winter due to enhanced NO₂ emissions from coal-fired winter heating, a longer photochemical lifetime and atmospheric transport. Daily maximum and minimum NO₂ VCDs were observed, independent of the season, at around 11:00 and 13:00 local time, respectively, and the most obvious increases and decreases occurred in the winter and autumn, respectively. The mean diurnal NO₂ VCDs at 11:00 increased to at 08:00 by 1.6, 5.8, and 6.7 × 10¹⁵ molecules cm⁻² in the summer, autumn and winter, respectively, due to increased NO₂ emissions, and then decreased by 2.8, 4.2, and 5.1 × 10¹⁵ molecules cm⁻² at 13:00 in the spring, summer, and autumn, respectively. This was due to strong solar radiation and increased planetary boundary layer height. There was no obvious weekend effect, and the NO₂ VCDs only decreased by about 10% on the weekends. We evaluated the contributions of emissions and transport in the different seasons to the NO₂ VCDs using a generalized additive model, where the contributions of local emissions to the total in the spring, summer, autumn, and winter were 89 ± 12%, 92 ± 11%, 86 ± 12%, and 72 ± 16%, respectively. The contribution of regional transport reached 26% in the winter, and this high contribution value was mainly correlated with the northeast wind, which was due to the transport channel of air pollutants along the Changbai Mountains in NEC. The NO₂/SO₂ ratio was used to identify NO₂ from industrial sources and vehicle exhaust. The contribution of industrial NO₂ VCD sources was >66.3 ± 16% in Shenyang due to the large amount of coal combustion from heavy industrial activity, which emitted large amounts of NO₂. Our results suggest that air quality management in Shenyang should consider reductions in local NO₂ emissions from industrial sources along with regional cooperative control.

It is enlightening to determine the discrepancies and potential reasons for the degree of impact from the COVID-19 control measures on air quality as well as the associated health and economic impacts. Analysis of air quality, socio-economic factors, and meteorological data from 447 cities in 46 countries indicated that the COVID-19 control measures had significant impacts on the PM₂.₅ (particulate matter with an aerodynamic diameter less than 2.5 μm) concentrations in 20 (reduced PM₂.₅ concentrations of −7.4–29.1 μg m⁻³) of the selected 46 countries. In these 20 countries, the robustly distinguished changes in the PM₂.₅ concentrations caused by the control measures differed between the developed (95% confidence interval (CI): −2.7–5.5 μg m⁻³) and developing countries (95% CI: 8.3–23.2 μg m⁻³). As a result, the COVID-19 lockdown reduced death and hospital admissions change from the decreased PM₂.₅ concentrations by 7909 and 82,025 cases in the 12 developing countries, and by 78 and 1214 cases in the eight developed countries. The COVID-19 lockdown reduced the economic cost from the PM₂.₅ related health burden by 54.0 million dollars in the 12 developing countries and by 8.3 million dollars in the eight developed countries. The disparity was related to the different chemical compositions of PM₂.₅. In particular, the concentrations of primary PM₂.₅ (e.g., BC) in cities of developing countries were 3–45 times higher than those in developed countries, so the mass concentration of PM₂.₅ was more sensitive to the reduced local emissions in developing countries during the COVID-19 control period. The mass fractions of secondary PM₂.₅ in developed countries were generally higher than those in developing countries. As a result, these countries were more sensitive to the secondary atmospheric processing that may have been enhanced due to reduced local emissions.

Air pollution is a leading public health concern around the world. Assessing the public's knowledge about air quality is critical to calibrate public health interventions. However, previous efforts to measure knowledge about air quality (AQIQ) have not relied on consistent and validated measures, thus precluding cross-country comparisons. We aimed to develop a robust scale to assess AQIQ and tested it in multiple countries. To evaluate the psychometric properties and select the best performing items out of 10 AQIQ questions, we used methods from classical test theory and item response theory. We evaluated the scales using several scalability measures, including the Kuder-Richardson Formula 20 (KR-20), Loevinger's H, as well as trace lines. Volunteers from the United States (US, n = 400), India (n = 403), and China (n = 443) were recruited to validate the scale. Multiple linear regression was used to estimate the association between demographic factors and AQIQ. We found that participants from India had the highest AQIQ. In addition, not all questions performed well in each country. The scale was pruned and shorter subscales were validated. In the US, we obtained a 4-item scale (KR20 = 0.53, Loevinger's H = 0.34). In India, we obtained a 6-item scale (KR20 = 0.56; Loevinger's H = 0.48 for just 2 items). In China, we obtained a 5-item scale (KR20 = 0.39; Loevinger's H = 0.41 for just 2 items). Compared to the 10-item scale, the pruned scales showed stronger associations between measures of socioeconomic status and AQIQ. The results were robust to the scale used. Overall, general knowledge questions measured AQIQ more effectively in the US and India whereas knowledge of the air quality index better measured AQIQ in China. The findings suggest that careful measurement and validation are essential to develop knowledge scales for use in public health and environmental research.

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 PM₂.₅ 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 PM₂.₅ 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 PM₂.₅ 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 PM₂.₅ concentrations were mainly the results of dry deposition accelerated by increases in LAI, which outweighed the ozone and PM₂.₅ formations via BVOC-driven chemistry. Some inland regions showed further decreases in PM₂.₅ concentrations due to reduced reactions of PM₂.₅ precursors with hydroxyl radicals depleted by BVOCs. The reductions in sea surface ozone and PM₂.₅ concentrations were accompanied by the reductions in those in upwind inland regions, which led to less ozone and PM₂.₅ inflows. The results suggest the importance of the selective use of vegetation parameters for air quality modeling.

The Community Multi-Scale Air Quality (CMAQ) model was applied to evaluate the air quality in the coastal city of Kannur, India, during the 2020 COVID-19 lockdown. From the Pre1 (March 1–24, 2020) period to the Lock (March 25–April 19, 2020) and Tri (April 20–May 9, 2020) periods, the Kerala state government gradually imposed a strict lockdown policy. Both the simulations and observations showed a decline in the PM₂.₅ concentrations and an enhancement in the O₃ concentrations during the Lock and Tri periods compared with that in the Pre1 period. Integrated process rate (IPR) analysis was employed to isolate the contributions of the individual atmospheric processes. The results revealed that the vertical transport from the upper layers dominated the surface O₃ formation, comprising 89.4%, 83.1%, and 88.9% of the O₃ sources during the Pre1, Lock, and Tri periods, respectively. Photochemistry contributed negatively to the O₃ concentrations at the surface layer. Compared with the Pre1 period, the O₃ enhancement during the Lock period was primarily attributable to the lower negative contribution of photochemistry and the lower O₃ removal rate by horizontal transport. During the Tri period, a slower consumption of O₃ by gas-phase chemistry and a stronger vertical import from the upper layers to the surface accounted for the increase in O₃. Emission and aerosol processes constituted the major positive contributions to the net surface PM₂.₅, accounting for a total of 48.7%, 38.4%, and 42.5% of PM₂.₅ sources during the Pre1, Lock, and Tri periods, respectively. The decreases in the PM₂.₅ concentrations during the Lock and Tri periods were primarily explained by the weaker PM₂.₅ production from emission and aerosol processes. The increased vertical transport rate of PM₂.₅ from the surface layer to the upper layers was also a reason for the decrease in the PM₂.₅ during the Lock periods.

Various precursor emissions and chemical mechanisms for secondary organic aerosol (SOA) formation were incorporated into a regional air quality model system (RAQMS) and applied to investigate the distribution, composition, and source contribution of SOA over east China in summer 2018. Model comparison against a variety of observations at a national scale demonstrated that the model was able to reasonably reproduce meteorological variables, O₃ and PM₂.₅ concentrations, and the model simulated SOA concentration generally agreed with observations, with the overall NMB of 7.0% and R of 0.4 in 10 cities over east China. The simulated period-mean SOA concentrations of 4–15 μg m⁻³ were mainly distributed over the North China Plain (NCP), the middle and lower reaches of the Yangtze River and Chongqing district. SOA dominated organic aerosol (OA) over China in summertime (90%). The percentage contributions to SOA from ASOA (SOA produced from anthropogenic volatile organic compounds (AVOC)), BSOA (SOA produced from biogenic volatile organic compounds (BVOC)), DSOA (SOA produced from aqueous uptake of glyoxal and methylglyoxal) and S/I-SOA (SOA produced from semi-volatile and intermediate volatile organic compounds) were estimated to be 48.3%, 28.6%, 14.3%, and 8.8% respectively, over east China in summertime. In terms of domain and period average, ASOA contributed most to SOA (59%) in north China, while BSOA contributed most to SOA (37.3%) in northeast China. The percentage contribution of DSOA to SOA reached 21.5% in southwest China. S/I-SOA accounted for approximately 10% of SOA in most areas of east China. This study reveals that while AVOC dominates SOA formation on average over east China, the SOA source contributions differ considerably in different regions of China. BVOC makes the same contribution to SOA formation as AVOC in northeast China and southwest China, where forest coverage and BVOC emission are higher and anthropogenic emissions are relatively low, highlighting the significant role of BVOC in summer SOA formation in China.

South Korea has experienced a rapid increase in ozone concentrations in surface air together with China for decades. Here we use a 3-D global chemical transport model, GEOS-Chem nested over East Asia (110 E - 140 E, 20 N–50 N) at 0.25° × 0.3125° resolution, to examine locally controllable (domestic anthropogenic) versus uncontrollable (background) contributions to ozone air quality at the national scale for 2016. We conducted model simulations for representative months of each season: January, April, July, and October for winter, spring, summer, and fall and performed extensive model evaluation by comparing simulated ozone with observations from satellite and surface networks. The model appears to reproduce observed spatial and temporal ozone variations, showing correlation coefficients (0.40–0.87) against each observation dataset. Seasonal mean ozone concentrations in the model are the highest in spring (39.3 ± 10.3 ppb), followed by summer (38.3 ± 14.4 ppb), fall (31.2 ± 9.8 ppb), and winter (24.5 ± 7.9 ppb), which is consistent with that of surface observations. Background ozone concentrations obtained from a sensitivity model simulation with no domestic anthropogenic emissions show a different seasonal variation in South Korea, showing the highest value in spring (46.9 ± 3.4 ppb) followed by fall (38.2 ± 3.7 ppb), winter (33.0 ± 1.9 ppb), and summer (32.1 ± 6.7 ppb). Except for summer, when the photochemical formation is dominant, the background ozone concentrations are higher than the seasonal ozone concentrations in the model, indicating that the domestic anthropogenic emissions play a role as ozone loss via NOₓ titration throughout the year. Ozone air quality in South Korea is determined mainly by year-round regional background contributions (peak in spring) with summertime domestic ozone formation by increased biogenic VOCs emissions with persistent NOₓ emissions throughout the year. The domestic NOₓ emissions reduce MDA8 ozone around large cities (Seoul and Busan) and hardly increase MDA8 in other regions in spring, but it increases MDA8 across the country in summer. Therefore, NOₓ reduction can be effective in control of MDA8 ozone in summer, but it can have rather countereffect in spring.

Open waste burning emissions constitute a significant source of air pollution affecting human health in India. In regions where cleaner fuels have displaced solid biofuel usage, open waste burning is rapidly becoming one of the largest sources of airborne human class-I-carcinogens and particulate matter. As the establishment of waste management infrastructure in rural India is likely to take years, we explore whether health-relevant emissions can be reduced by legalizing the burning of dry non-biodegradable waste in improved devices. We measure the emission factors of 76 VOCs, CH₄, CO, and CO₂ from different types of waste burned in two different improved devices, a burn basket and a local water heater. Based on our experiments, we create four “what-if” intervention scenarios to assess the improvement of air quality due to the emission reductions that can be accomplished by four management strategies. We find that substituting the traditional, more polluting water heating fuels with dry plastic waste across rural India can reduce primary emissions (e.g., −29 Ggy⁻¹ for benzene) and ozone formation potential (−2960 Ggy⁻¹) from open waste burning. When dry waste is used in lieu of more polluting fuels, and its burning serves a purpose, the net class-I-carcinogen benzene emissions, would be halved compared to the present. The change in emissions for the class-I carcinogen 1,3-butadiene would become net negative. This happens because the emissions avoided when part of the solid biofuel currently used in rural India is replaced by plastic waste (4.1 (1.2–4.1) Ggy⁻¹) exceed the waste burning emissions of this compound (3 (1.2–3.7) Ggy⁻¹) by so much, that residential sector emission reductions offset all waste burning emissions including those of landfill fires. Our study demonstrates that India's air quality can be improved by permitting and promoting the use of dry packaging waste in lieu of traditional biofuels and by promoting improved burning devices.

During the COVID-19 lockdown, atmospheric PM₂.₅ in the Pearl River Delta (PRD) showed the highest reduction in China, but the reasons, being a critical question for future air quality policy design, are not yet clear. In this study, we analyzed the relationships among gaseous precursors, secondary aerosols and atmospheric oxidation capacity in Shenzhen, a megacity in the PRD, during the lockdown period in 2020 and the same period in 2021. The comprehensive observational datasets showed large lockdown declines in all primary and secondary pollutants (including O₃). We found that, however, the daytime concentrations of secondary aerosols during the lockdown period and normal period were rather similar when the corresponding odd oxygen (Oₓ≡O₃+NO₂, an indicator of photochemical processing avoiding the titration effect of O₃ by freshly emitted NO) were at similar levels. Therefore, reduced Oₓ, rather than the large reduction in precursors, was a direct driver to achieve the decline in secondary aerosols. Moreover, Oₓ was also found to determine the spatial distribution of intercity PM₂.₅ levels in winter PRD. Thus, an effective strategy for winter PM₂.₅ mitigation should emphasize on control of winter O₃ formation in the PRD and other regions with similar conditions.