Based on ambient air quality data, meteorological observation data, and satellite remote sensing data, the temporal and spatial variations in ozone (O3) pollution, the sensitivity of O3, and its relationship with meteorological factors in Hainan Island were analyzed in this study. The results showed that the maximum daily 8-h moving mean (O3-8h) in western and northern cities in Hainan Island was higher than that in the central, eastern, and southern cities. O3-8h was the highest in 2015, and O3-8h exceeding the standard proportion was the largest in 2019. In addition, O3-8h was positively correlated with average temperature (P<0.1), sunshine duration (P<0.01), total solar radiation (P<0.01), atmospheric pressure, and average wind speed and was negatively correlated with precipitation (P<0.05) and relative humidity. The satellite remote sensing data showed that the tropospheric NO2 column concentration (NO2-OMI) and HCHO column concentration (HCHO-OMI) displayed opposite trends in Hainan Island from 2015 to 2020. Compared with those in 2015, NO2-OMI increased by 7.74% and HCHO-OMI decreased by 10.2% in 2020. Moreover, Hainan Island belongs to the NOx control area, and the FNR value exhibited a fluctuating downward trend in the past 6 years, with a trend coefficient and climatic trend rate of -0.514 and -0.123 a-1, respectively. A strong correlation was observed between meteorological factors and the FNR value of Hainan Island.

The surface ozone (O3) spatiotemporal distribution, variations, and its causes in Ji\'nan from 2015 to 2020 were revealed based on the air quality monitoring network data and satellite retrievals from the Ozone Monitoring Instrument (OMI). The results showed that the ozone concentration in Ji\'nan gradually increased from 2015 to 2020. The annual 90th percentile of the daily maximum 8-h average (MDA8) O3(namely the annual evaluation value) and the MDA8 O3(April-September) increased by 4.8 μg·(m3·a)-1 and 3.8 μg·(m3·a)-1, respectively. The trend of the ozone levels in the high-concentration range increased faster than that in the low-concentration range. The MDA8 in June increased by 7.4 μg·(m3·a)-1, and the rate range of increases was 2.6-3.9 μg·(m3·a)-1 in the cool seasons (December-February); thus, the O3 control in winter cannot be ignored. It is apparent from the diurnal variations in ozone from 2015 to 2020 in April-September that the average ozone levels have risen in recent years. The growth rate in the daytime was higher than that at night. The capacity of photochemical production has been increasing, especially in recent years. Additionally, it is noteworthy that the peak time for ozone levels occurred approximately 1-2 h earlier. The disparity of ozone concentrations among different stations gradually decreased in recent years. Compared with that in 2015, the range of areas with high O3 concentrations in 2019-2020 was further expanded. The significant positive trends in MDA8-90th and MDA8 (April-September) were observed in 16.1% and 22.6% of the monitoring sites in Ji\'nan (P<0.05), most of which were located in urban areas and the suburbs close to urban areas. The temporal and spatial changes in ozone in Jinan had been affected by the changes in VOCs and NOx emissions since 2015. Satellite remote sensing data from 2015 to 2020 revealed that the NO2 tropospheric columns (April-September) showed reductions of 20.6%, with a decreasing rate of 0.3×1015 mole·(cm2·a)-1, especially in the urban areas and suburbs. The detected variation trends of tropospheric HCHO were weak and insignificant, which suggested that the decrease in NOx emissions was much greater than the decrease in VOCs emissions, and the gap had become more obvious in the urban areas. With responses to precursor emissions, the chemical sensitivity of O3 formation had been changing. The VOCs-limited regimes continuously decreased, and the mixed NOx/VOCs-sensitive regimes and NOx-limited regimes increased. In general, such an extremely inappropriate control ratio of ozone precursor NOx/VOCs led to an overall trend of slow increasing fluctuations of O3 in Ji\'nan. The findings clearly indicate that the reduction of VOCs in Ji\'nan was far from sufficient, and strengthening the current control of VOCs emissions is an effective measure to control the growth trend of O3 pollution in Ji\'nan in the near future, especially in urban and surrounding suburban areas.

Based on air quality station data and satellite remote sensing data, the interannual variation characteristics and seasonal variation trends of near-surface ozone (O3) in Henan province were studied, and the variation in O3 sensitivity was analyzed. The results showed that the O3 concentration near the surface of Henan province increased first and then decreased from 2015 to 2020. The highest O3 concentration was found in 2018, and the annual mean of the maximum daily 8 h moving mean (MDA8) of O3 was 110.70 μg·m-3. The difference in MDA8 values among different stations gradually decreased. From 2015 to 2020, the average monthly MDA8 in Henan province showed an upward trend, with a growth rate of 2.46 μg·(m3·a)-1. According to the MK trend test, except for in Luohe, Nanyang, and Pingdingshan, the rising trend in other cities was significant (P<0.05). The concentration of MDA8 in the four seasons also showed an increasing trend during the 6 years as follows:autumn (19.31%)>winter (17.09%)>spring (16.82%)>summer (7.24%). From 2015 to 2019, the high value of tropospheric NO2 was concentrated in the northwest of Henan province, and the concentration showed a decreasing trend with a decreasing rate of 0.34×1015 molecules·(cm2·a)-1, whereas the tropospheric HCHO showed a slow rising trend with an annual growth rate of 0.19×1015 molecules·(cm2·a)-1, with a higher concentration in the northern urban area. The O3 sensitivity control area from 2015 to 2019 showed that most of the eastern part of Henan province belonged to the VOCs limited category.

Comprehensive air quality model with extensions (CAMx)-decoupled direct method (DDM) was used to simulate ozone-NOx-VOCs sensitivity of for May-November in 2016-2018 in China. Based on the relationship between the simulated ozone (O3) sensitivity values and the ratio of formaldehyde (HCHO) to NO2 (FNR) and the ratio of production rate of hydrogen peroxide (H2O2) to production rate of nitric acid (HNO3) ( [Formula: see text] ), the localized range of FNR and [Formula: see text] thresholds in different regions in China were obtained. The overall simulated FNR values are about 1.640-2.520, and [Formula: see text] values are about 0.540-0.830 for the transition regime. Model simulated O3 sensitivities or region specific FNR or [Formula: see text] thresholds should be applied to ensure the accurate local O3 sensitivity regimes. Using the tropospheric column FNR values from ozone monitoring instrument (OMI) satellite data as an indicator with the simulated threshold values, the spatial distributions of O3 formation regimes in China are determined. The O3 sensitivity regimes from eastern to central China are gradually from VOC-limited, transition to NOx-limited spatially, and moving toward to transition or NOx-limited regime from 2005 to 2019 temporally.

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Bayesian geostatistical regression (GR) models estimate air pollution exposure at high spatial resolution, quantify the prediction uncertainty and provide probabilistic inference on the exceedance of air quality thresholds. However, due to high computational burden, previous GR models have provided gridded ambient nitrogen dioxide (NO2) concentrations at smaller areas of investigation. Here, we applied these models to estimate yearly averaged NO2 concentrations at 1 km2 spatial resolution across 44 European countries, integrating information from in situ monitoring stations, satellites and chemical transport model (CTM) simulations. The tropospheric values of NO2 derived from the ozone monitoring instrument (OMI) onboard the National Aeronautics and Space Administration\'s (NASA\'s) Aura satellite were converted to near ground NO2 concentration proxies using simulations from the 3-D global CTM (GEOS-Chem) at 0.5° × 0.625°spatial resolution and surface-to-column NO2 ratios. Simulations from the Ensemble of regional CTMs at spatial resolution of 0.1° × 0.1°were extracted from the Copernicus atmosphere monitoring service (CAMS). The contribution of these covariates to the predictive capability of geostatistical models was for the first time evaluated here through a rigorous model selection procedure along with additional continental high-resolution satellite-derived products, including novel data from the pan-European Copernicus land monitoring service (CLMS). The results have shown that the conversion of columnar NO2 values to surface quasi-observations yielded models with slightly better predictive ability and lower uncertainty. Nonetheless, the use of higher resolution CAMS-Ensemble simulations as covariates in GR models granted the most accurate surface NO2 estimates, showing that, in 2016, 16.17 (95% C.I. 6.34-29.96) million people in Europe, representing 2.97% (95% C.I. 1.16% - 5.50%) of the total population, were exposed to levels above the EU directive and WHO air quality guidelines threshold for NO2. Our estimates are readily available to policy makers and scientists assessing the burden of disease attributable to NO2 in 2016.

Based on the ozone monitoring instrument (OMI)/Aura L2 OMAERUV data from 2008 to 2017, the spatial-temporal distribution of absorptive aerosols during the past 10 years were studied. The results are as follows. ① In the temporal distribution, the inter-annual variation of absorptive aerosol optical depth (AAOD) first increased and then decreased, reaching the highest value of 0.056 in 2011; this is consistent with the aerosol optical depth (AOD) of 0.702 in the Yangtze River Delta. The inter-monthly variation shows that the high value of AAOD appeared mostly in January, March, and June and increased significantly from November to January. ② In the spatial distribution, the AAOD was higher in the north than in the south in the Yangtze River Delta, and the AOD was similar to the AAOD. High values of AAOD above 0.05 were concentrated mainly in northern Anhui and Jiangsu provinces and in Nanjing, Hangzhou, and Jinhua. The seasonal spatial distribution of AAOD and AOD was higher in spring and winter and lower in autumn, although the AOD was very high and the AAOD was low in summer. The contribution of black carbon in the Yangtze River Delta was consistent with the annual spatial distribution of the AAOD and AOD.

An analysis is presented for both ground- and satellite-based retrievals of total column ozone and nitrogen dioxide levels from the Washington, D.C., and Baltimore, Maryland, metropolitan area during the NASA-sponsored July 2011 campaign of Deriving Information on Surface COnditions from Column and VERtically Resolved Observations Relevant to Air Quality (DISCOVER-AQ). Satellite retrievals of total column ozone and nitrogen dioxide from the Ozone Monitoring Instrument (OMI) on the Aura satellite are used, while Pandora spectrometers provide total column ozone and nitrogen dioxide amounts from the ground. We found that OMI and Pandora agree well (residuals within ±25 % for nitrogen dioxide, and ±4.5 % for ozone) for a majority of coincident observations during July 2011. Comparisons with surface nitrogen dioxide from a Teledyne API 200 EU NOx Analyzer showed nitrogen dioxide diurnal variability that was consistent with measurements by Pandora. However, the wide OMI field of view, clouds, and aerosols affected retrievals on certain days, resulting in differences between Pandora and OMI of up to ±65 % for total column nitrogen dioxide, and ±23 % for total column ozone. As expected, significant cloud cover (cloud fraction >0.2) was the most important parameter affecting comparisons of ozone retrievals; however, small, passing cumulus clouds that do not coincide with a high (>0.2) cloud fraction, or low aerosol layers which cause significant backscatter near the ground affected the comparisons of total column nitrogen dioxide retrievals. Our results will impact post-processing satellite retrieval algorithms and quality control procedures.