Ozone profile retrievals from TROPOMI: Implication for the variation of tropospheric ozone during the outbreak of COVID-19 in China

Parsimonious estimation of hourly surface ozone concentration across China during 2015-2020

Surface ozone is an important air pollutant detrimental to human health and vegetation productivity, particularly in China. However, high resolution surface ozone concentration data is still lacking, largely hindering accurate assessment of associated environmental impacts. Here, we collected hourly ground ozone observations (over 6 million records), remote sensing products, meteorological data, and social-economic information, and applied recurrent neural networks to map hourly surface ozone data (HrSOD) at a 0.1° × 0.1° resolution across China during 2015-2020. The coefficient of determination (R2) values in sample-based, site-based, and by-year cross-validations were 0.72, 0.65 and 0.71, respectively, with the root mean square error (RMSE) values being 11.71 ppb (mean = 30.89 ppb), 12.81 ppb (mean = 30.96 ppb) and 11.14 ppb (mean = 31.26 ppb). Moreover, it exhibits high spatiotemporal consistency with ground-level observations at different time scales (diurnal, seasonal, annual), and at various spatial levels (individual sites and regional scales). Meanwhile, the HrSOD provides critical information for fine-resolution assessment of surface ozone impacts on environmental and human benefits.

Vertical Evolution of Ozone Formation Sensitivity Based on Synchronous Vertical Observations of Ozone and Proxies for Its Precursors: Implications for Ozone Pollution Prevention Strategies

Photochemical ozone (O3) formation in the atmospheric boundary layer occurs at both the surface and elevated altitudes. Therefore, the O3 formation sensitivity is needed to be evaluated at different altitudes before formulating an effective O3 pollution prevention and control strategy. Herein, we explore the vertical evolution of O3 formation sensitivity via synchronous observations of the vertical profiles of O3 and proxies for its precursors, formaldehyde (HCHO) and nitrogen dioxide (NO2), using multi-axis differential optical absorption spectroscopy (MAX-DOAS) in urban areas of the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) regions in China. The sensitivity thresholds indicated by the HCHO/NO2 ratio (FNR) varied with altitude. The VOC-limited regime dominated at the ground level, whereas the contribution of the NOx-limited regime increased with altitude, particularly on heavily polluted days. The NOx-limited and transition regimes played more important roles throughout the entire boundary layer than at the surface. The feasibility of extreme NOx reduction to mitigate the extent of the O3 pollution was evaluated using the FNR-O3 curve. Based on the surface sensitivity, the critical NOx reduction percentage for the transition from a VOC-limited to a NOx-limited regime is 45-72%, which will decrease to 27-61% when vertical evolution is considered. With the combined effects of clean air action and carbon neutrality, O3 pollution in the YRD and PRD regions will transition to the NOx-limited regime before 2030 and be mitigated with further NOx reduction.

High-resolution estimation of near-surface ozone concentration and population exposure risk in China

Considering the spatial and temporal effects of atmospheric pollutants, using the geographically and temporally weighted regression and geo-intelligent random forest (GTWR-GeoiRF) model and Sentinel-5P satellite remote sensing data, combined with meteorological, emission inventory, site observation, population, elevation, and other data, the high-precision ozone concentration and its spatiotemporal distribution near the ground in China from March 2020 to February 2021 were estimated. On this basis, the pollution status, near-surface ozone concentration, and population exposure risk were analyzed. The findings demonstrate that the estimation outcomes of the GTWR-GeoiRF model have high precision, and the precision of the estimation results is higher compared with that of the non-hybrid model. The downscaling method enhances estimation results to some extent while addressing the issue of limited spatial resolution in some data. China's near-surface ozone concentration distribution in space shows obvious regional and seasonal characteristics. The eastern region has the highest ozone concentrations and the lowest in the northeastern region, and the wintertime low is higher than the summertime high. There are significant differences in ozone population exposure risks, with the highest exposure risks being found in China's eastern region, with population exposure risks mostly ranging from 0.8 to 5.

Assessment of variations in air quality in cities of Ecuador in relation to the lockdown due to the COVID-19 pandemic

This study analyzes the effect of lockdown due to COVID-19 on the spatiotemporal variability of ozone (O₃), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂) concentrations in different provinces of continental Ecuador using satellite information from Sentinel - 5P. The statistical analysis includes data from 2018 to March 2021 and was performed based on three periods defined a priori: before, during, and after lockdown due to COVID-19, focusing on the provinces with the highest concentrations of the studied gases (hotspots). The results showed a significant decrease in NO₂ concentrations during the COVID-19 lockdown period in all the study areas: the Metropolitan District of Quito (DMQ) and the provinces of Guayas and Santo Domingo de los Tsáchilas. In the period after lockdown, NO₂ concentrations increased by over 20% when compared to the pre-lockdown period, which may be attributable to a shift towards private transportation due to health concerns. On the other hand, SO₂ concentrations during the lockdown period showed irregular, non-significant variations; however, increases were observed in the provinces of Chimborazo, Guayas, Santa Elena, and Morona Santiago, which could be partly attributed to the eruptive activity of the Sangay volcano during 2019-2020. Conversely, O₃ concentrations increased by 2-3% in the study areas; this anomalous behavior could be attributed to decreased levels of NO_x, which react with ozone, reducing its concentration. Finally, satellite data validation using the corresponding data from monitoring stations in the DMQ showed correlation values of 0.9 for O₃ data and 0.7 for NO₂ data, while no significant correlation was found for SO₂.

Vertical distributions and potential sources of wintertime atmospheric pollutants and the corresponding ozone production on the coast of Bohai Sea

This study investigated the wintertime vertical distributions and source areas of aerosols, NO₂, and HCHO in a coastal city of Dongying from December 2020 to March 2021, using ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) and a potential source contribution function (PSCF) model, respectively. Moreover, the chemical production sensitivity of O₃ at different height layers was analyzed using HCHO/NO₂ ratios. The results revealed that the wintertime averaged highest concentrations of aerosol (1.25 km^-1), NO₂ (14.81 ppb), and HCHO (2.32 ppb) were mainly distributed at the surface layer, 100-200 m layer, and 200-300 m layer, respectively. Regarding the diurnal cycles, high concentrations of aerosol (>1.4 km^-1) and NO₂ (>16.0 ppb) usually appeared in the early morning and late afternoon, while high concentrations of HCHO (>2.5 ppb) usually occurred during 12:00-15:00. The PSCF model revealed that the wintertime aerosol mainly originated from Shandong, northern Jiangsu, Korea, and the northwestern Mongolian Plateau. Below 200 m, NO₂ was mainly from western Shandong, whereas above 600 m, it was mainly from northern Shandong and the Beijing-Tianjin-Hebei (BTH) region. The corresponding sources for HCHO were central and southern Shandong (below 200 m) and northern Shandong, northern Jiangsu, and southeastern BTH (above 600 m). In addition, the chemical production sensitivity of O₃ below 100 m was observed only in the VOC-limited regime. The percentages of O₃ production under the NO_x-limited, NO_x-VOC-limited, and VOC-limited regimes were 10.75% (31.18%), 4.30% (19.35%), and 84.95% (49.47%) at the 500-600 m (900-1000 m) layer. This study has guiding significance for the coordinated control of PM_2.5 and O₃, and can assist in the implementation of regional joint prevention and control strategies for air pollutants.

Impacts of TROPOMI-Derived NO X Emissions on NO2 and O3 Simulations in the NCP during COVID-19

NO₂ and O₃ simulations have great uncertainties during the COVID-19 epidemic, but their biases and spatial distributions can be improved with NO₂ assimilations. This study adopted two top-down NO _X inversions and estimated their impacts on NO₂ and O₃ simulation for three periods: the normal operation period (P1), the epidemic lockdown period following the Spring Festival (P2), and back to work period (P3) in the North China Plain (NCP). Two TROPOspheric Monitoring Instrument (TROPOMI) NO₂ retrievals came from the Royal Netherlands Meteorological Institute (KNMI) and the University of Science and Technology of China (USTC), respectively. Compared to the prior NO _X emissions, the two TROPOMI posteriors greatly reduced the biases between simulations with in situ measurements (NO₂ MREs: prior 85%, KNMI -27%, USTC -15%; O₃ MREs: Prior -39%, KNMI 18%, USTC 11%). The NO _X budgets from the USTC posterior were 17-31% higher than those from the KNMI one. Consequently, surface NO₂ levels constrained by USTC-TROPOMI were 9-20% higher than those by the KNMI one, and O₃ is 6-12% lower. Moreover, USTC posterior simulations showed more significant changes in adjacent periods (surface NO₂: P2 vs P1, -46%, P3 vs P2, +25%; surface O₃: P2 vs P1, +75%, P3 vs P2, +18%) than the KNMI one. For the transport flux in Beijing (BJ), the O₃ flux differed by 5-6% between the two posteriori simulations, but the difference of NO₂ flux between P2 and P3 was significant, where the USTC posterior NO₂ flux was 1.5-2 times higher than the KNMI one. Overall, our results highlight the discrepancies in NO₂ and O₃ simulations constrained by two TROPOMI products and demonstrate that the USTC posterior has lower bias in the NCP during COVD-19.

Socioeconomic development role in hospitalization related to air pollution and meteorology: A study case in southern Brazil

Air pollution is one of the foremost environmental threats to human health. However, the meteorological and social factors that lead to respiratory and cardiovascular diseases have not been fully elucidated. In this study, we use Principal Component Analysis and Generalized Linear Model (PCA-GLM) to investigate the combined effect of socioeconomic development and air pollution on cardiorespiratory hospitalization in southern Brazil. This region has the highest rates of hospitalization by cardiorespiratory diseases in the country. We analyze three main sources of data: (i) air pollutants density from TROPOMI/Sentinel-5p satellite; (ii) temperature, humidity, and planetary boundary layer height (PBLH) modeled with the Weather Research Forecast model; and (iii) hospitalization by cardiorespiratory diseases obtained from the Brazilian National Health System. We estimate the Relative Risk (RR) using the PCA-GLM coefficients and interquartile variations of air pollutants density and meteorological parameters. Our results show that the population living in colder and drier municipalities is more prone to cardiorespiratory hospitalization. Regarding respiratory hospitalization, municipalities with lower socioeconomic development are more sensitive to meteorology and pollution variability than highly developed ones. In less developed municipalities, we observe the highest rates of cardiorespiratory hospitalization even if air pollution is low, which we interpret in terms of higher vulnerability. The RR analysis suggests that air pollution is an important environmental risk to cardiovascular diseases and respiratory diseases is more sensitive to air pollution and meteorology than cardiovascular ones. Our findings corroborate the mounting evidence that social vulnerability is a significant factor affecting the increase of cardiorespiratory hospitalization in the world.

Spatiotemporal Variations in the Air Pollutant NO2 in Some Regions of Pakistan, India, China, and Korea, before and after COVID-19, Based on Ozone Monitoring Instrument Data

In 2020, COVID-19 was proclaimed a pandemic by the World Health Organization, prompting several nations throughout the world to block their borders and impose a countrywide lockdown, halting all major manmade activities and thus leaving a beneficial impact on the natural environment. We investigated the influence of a sudden cessation of human activity on tropospheric NO2 concentrations to understand the resulting changes in emissions, particularly from the power-generating sector, before (2010–2019) and during the pandemic (2020). NO2 was chosen because of its short lifespan in the Earth’s atmosphere. Using daily tropospheric NO2 column concentrations from the Ozone Monitoring Instrument, the geographic and temporal characteristics of tropospheric NO2 column were investigated across 12 regions in India, Pakistan, China, and South Korea (2010–2020). We analyzed weekly, monthly, and annual trends and found that the NO2 concentrations were decreased in 2020 (COVID-19 period) in the locations investigated. Reduced anthropogenic activities, including changes in energy production and a reduction in fossil fuel consumption before and during the COVID-19 pandemic, as well as reduced traffic and industrial activity in 2020, can explain the lower tropospheric NO2 concentrations. The findings of this study provide a better understanding of the process of tropospheric NO2 emissions over four nations before and after the coronavirus pandemic for improving air quality modeling and management approaches.

Deciphering waste bound nitrogen by employing psychrophillic Aporrectodea caliginosa and priming of coprolites by associated heterotrophic nitrifiers under high altitude Himalayas

Himalayan ecosystem is characterized by its fragile climate with rich repositories of biodiversity. Waste collection and disposal are becoming increasingly difficult due to topographical variations. Aporrectodea caligenosa, a versatile psychrophillic soil dweller, is a useful biocatalyst with potent bio-augmented capability for waste treatment at low temperatures. Microcosm experiments were conducted to elucidate the comprehensive nature of biogenic nitrogen transformation to NH₄⁺ and NO₃⁻ produced by coupling of earthworm-microbes. Higher biogenic recovery of NH₄⁺-N from coprolites of garden soil (47.73 ± 1.16%) and Himalayan goat manure (86.32 ± 0.92%) with an increment of 14.12 and 47.21% respectively over their respective control (without earthworms) with a linear decline beyond 4th week of incubation was reported. NO₃^–-N recovery progressively sustained in garden soil and goat manure coprolites during entire incubation with highest 81.81 ± 0.45 and 87.20 ± 1.08 µg-N g⁻¹dry weight recorded in 6th and 5th week of incubation respectively and peak increments as 38.58 and 53.71% relative to respective control (without earthworms). Declined NH₄⁺–N in coprolites at low temperature (15.0 ± 2.0 °C) evidenced increased nitrification rates by taking over the process by abundant nitrifying microbes. Steady de-nitrification with progressive incubation on an average was 16.95 ± 0.46 ng-N g⁻¹ per week and 21.08 ± 0.87 ng-N g⁻¹ per week compared to 14.03 ± 0.58 ng-N g⁻¹ per week and 4.50 ± 0.31 ng-N g⁻¹ per week in respective control treatments. Simultaneous heterotrophic nitrification and aerobic denitrification (SHNAD) was found to be a prominent bioprocess at low temperature that resulted in high and stable total nitrogen and nitrate accumulation from garden soil and goat manure with relative recovery efficiency of 11.12%, 14.97% and 14.20%; 19.34%. A. caligenosa shows promising prospects for mass applicability in biogenic N removal from manure of Himalayan goat.

Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling

Spatiotemporal patterns and trends of COVID-19 at a local spatial scale using Bayesian approaches are hardly observed in literature. Also, studies rarely use satellite-derived long time-series data on the environment to predict COVID-19 risk at a spatial scale. In this study, we modelled the COVID-19 pandemic risk using a Bayesian hierarchical spatiotemporal model that incorporates satellite-derived remote sensing data on land surface temperature (LST) from January 2020 to October 2021 (89 weeks) and several socioeconomic covariates of the 140 neighbourhoods in Toronto. The spatial patterns of risk were heterogeneous in space with multiple high-risk neighbourhoods in Western and Southern Toronto. Higher risk was observed during Spring 2021. The spatiotemporal risk patterns identified 60% of neighbourhoods had a stable, 37% had an increasing, and 2% had a decreasing trend over the study period. LST was positively, and higher education was negatively associated with the COVID-19 incidence. We believe the use of Bayesian spatial modelling and the remote sensing technologies in this study provided a strong versatility and strengthened our analysis in identifying the spatial risk of COVID-19. The findings would help in prevention planning, and the framework of this study may be replicated in other highly transmissible infectious diseases.

Globally ubiquitous negative effects of nitrogen dioxide on crop growth

Nitrogen oxides (NO_x) are among the most widely emitted pollutants in the world, yet their impacts on agriculture remain poorly known. NO_x can directly damage crop cells and indirectly affect growth by promoting ozone (O₃) and aerosol formation. We use satellite measures of both crop greenness and NO_x during 2018-2020 to evaluate crop impacts for five major agricultural regions. We find consistent negative associations between NO₂ and greenness across regions and seasons. These effects are strongest in conditions where O₃ formation is NO_x limited but remain significant even in locations where this pathway is muted, suggesting a role for direct NO_x damage. Using simple counterfactuals and leveraging published relationships between greenness and growth, we estimate that reducing NO_x levels to the current fifth percentile in each region would raise yields by ~25% for winter crops in China, ~15% for summer crops in China, and up to 10% in other regions.

Airborne black carbon variations during the COVID-19 lockdown in the Yangtze River Delta megacities suggest actions to curb global warming

Airborne black carbon is a strong warming component of the atmosphere. Therefore, curbing black carbon emissions should slow down global warming. The 2019 coronavirus pandemic (COVID-19) is a unique opportunity for studying the response of black carbon to the varied human activities, in particular due to lockdown policies. Actually, there is few knowledge on the variations of black carbon in China during lockdowns. Here, we studied the concentrations of particulate matter (PM_2.5) and black carbon before, during, and after the lockdown in nine sites of the Yangtze River Delta in Eastern China. Results show 40-60% reduction of PM_2.5 and 40-50% reduction of black carbon during the lockdown. The classical bimodal peaks of black carbon in the morning and evening rush hours were highly weakened, indicating the substantial decrease of traffic activities. Contributions from fossil fuels combustion to black carbon decreased about 5-10% during the lockdown. Spatial correlation analysis indicated the clustering of the multi-site black carbon concentrations in the Yangtze River Delta during the lockdown. Overall, control of emissions from traffic and industrial activities should be efficient to curb black carbon levels in the frame of a 'green public transit system' for mega-city clusters such as the Yangtze River Delta.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10311-021-01327-3.

Impacts of the COVID-19 confinement on air quality, the Land Surface Temperature and the urban heat island in eight cities of Andalusia (Spain)

The COVID-19 outbreak and ensuing global lockdown situation have generated a very negative impact on the world economy, but they have also lent us a unique opportunity to research and better grasp the impacts of human activity on environmental pollution and urban climates. Such studies will be of vital importance for decision-making on measures needed to mitigate the effects of climate change in urban areas, in order to turn them into resilient environments. This study looks at eight cities in the region of Andalusia (southern Spain) to comprehensively assess their environmental quality with parameters (Pm10, So2, No2, Co and O3) obtained from meteorological stations. The aim was to determine how these parameters affect the Land Surface Temperature (LST) and the Surface Urban Heat Island (SUHI), on the basis of Sentinel 3 satellite thermal images. Knowing to what extent improved air quality can reduce the LST and SUHI of cities will be essential in the context of future environmental studies on which to base sustainable decisions. The geographic situation of cities in the Mediterranean Sea basin, highly vulnerable to climate change, and the high pollution rates and high daily temperature variations of these urban areas make them particularly attractive for analyses of this sort. During the confinement period, average reductions of some environmental pollutants were achieved: So2 (-33.5%), Pm10 (-38.3%), No2 (-44.0%) and Co (-26.5%). However, the environmental variable O3 underwent an average growth of 5.9%. The LST showed an average reduction of -4.6 °C (-19.3%), while the SUHI decreased by 1.02 °C (-59.8%). These values exhibit high spatio-temporal variations between day and night, and between inland and coastal cities.

A machine learning model to estimate ground-level ozone concentrations in California using TROPOMI data and high-resolution meteorology

Estimating ground-level ozone concentrations is crucial to study the adverse health effects of ozone exposure and better understand the impacts of ground-level ozone on biodiversity and vegetation. However, few studies have attempted to use satellite retrieved ozone as an indicator given their low sensitivity in the boundary layer. Using the Troposphere Monitoring Instrument (TROPOMI)'s total ozone column together with the ozone profile information retrieved by the Ozone Monitoring Instrument (OMI), as TROPOMI ozone profile product has not been released, we developed a machine learning model to estimate daily maximum 8-hour average ground-level ozone concentration at 10 km spatial resolution in California. In addition to satellite parameters, we included meteorological fields from the High-Resolution Rapid Refresh (HRRR) system at 3 km resolution and land-use information as predictors. Our model achieved an overall 10-fold cross-validation (CV) R² of 0.84 with root mean square error (RMSE) of 0.0059 ppm, indicating a good agreement between model predictions and observations. Model predictions showed that the suburb of Los Angeles Metropolitan area had the highest ozone levels, while the Bay Area and the Pacific coast had the lowest. High ozone levels are also seen in Southern California and along the east side of the Central Valley. TROPOMI data improved the estimate of extreme values when compared to a similar model without it. Our study demonstrates the feasibility and value of using TROPOMI data in the spatiotemporal characterization of ground-level ozone concentration.

Space-based monitoring of NO2 levels during COVID-19 lockdown in Cairo, Egypt and Riyadh, Saudi Arabia

The lockdown of COVID-19 pandemic has affected air quality due to the changes in human activities. Researchers worldwide observed reductions in NO₂ concentrations due to lockdown and related diminished human activities, notably the reduced industrial and vehicular use. However, limited information was available for the MENA Region. In this study, the impact of lockdown due to COVID-19 on NO₂ in two MENA major cities: Cairo, Egypt and Riyadh, Saudi Arabia was assessed. NO₂ column was retrieved from the Ozone Monitoring Instrument (OMI) on Aura for April 2017 to 2020. The monthly mean value of NO₂ concentrations of April 2017–2019 was used as a baseline. NO₂ concentration in April 2020 was compared to the baseline to assess the impact of lockdown on NO₂. The results demonstrated that the lockdown was associated with a reduction in NO₂ in both cities. NO₂ decreases by 40.3% and 23% in Riyadh and Cairo. By comparing the decrease of NO₂ at weekends and weekdays, it was found that in Cairo, the decrease in weekdays (16.3%) was lower than weekends (31.9%). While in Riyadh, the decrease in weekdays (43.9%) was higher than weekends (29.3%). Variation in the reduction rates appears to be related to the different lockdown regimens taken by the two countries, among other factors. The findings of the present investigation alert countries in the region about the impact of human activities on urban air population and urge them to take appropriate mitigation measures to maintain good ambient air quality to protect human health and the environment.

Improved Anthropogenic SO2 Retrieval from High-Spatial-Resolution Satellite and its Application during the COVID-19 Pandemic

Sulfur dioxide (SO₂) measured by satellites is widely used to estimate anthropogenic emissions. The Sentinel-5 Precursor (S-5P) operational SO₂ product is overestimated compared to the ground-based multiaxis differential optical absorption spectroscopy (MAX-DOAS) measurements in China and shows an opposite variation to the surface measurements, which limits the application of TROPOspheric monitoring instrument (TROPOMI) products in emissions research. Radiometric calibration, a priori profiles, and fitting windows might cause the overestimation of S-5P operational SO₂ product. Here, we improve the optimal-estimation-based algorithm through several calibration methods. The improved retrieval agrees reasonably well with the ground-based measurements (R > 0.70, bias <13.7%) and has smaller biases (-28.9%) with surface measurements over China and India. It revealed that the SO₂ column in March 2020 decreased by 51.6% compared to March 2019 due to the lockdown for curbing the spread of the COVID-19 pandemic, and there was a decrease of 50% during the lockdown than those after the lockdown, similar to the surface measurement trend, while S-5P operational SO₂ product showed an unrealistic increase of 19%. In India, the improved retrieval identified obvious "hot spots" and observed a 30% decrease of SO₂ columns during the lockdown.

Effects of COVID-19 on the environment: An overview on air, water, wastewater, and solid waste

The COVID-19 pandemic has hit the world hardly as of the beginning of 2020 and quickly spread worldwide from its first-reported point in early Dec. 2019. By mid-March 2021, the COVID-19 almost hit all countries worldwide, with about 122 and 2.7 million confirmed cases and deaths, respectively. As a strong measure to stop the infection spread and deaths, many countries have enforced quarantine and lockdown of many activities. The shutdown of these activities has resulted in large economic losses. However, it has been widely reported that these measures have resulted in improved air quality, more specifically in highly polluted areas characterized by massive population and industrial activities. The reduced levels of carbon, nitrogen, sulfur, and particulate matter emissions have been reported and confirmed worldwide in association with lockdown periods. On the other hand, ozone levels in ambient air have been found to increase, mainly in response to the reduced nitrogen emissions. In addition, improved water quality in natural water resources has been reported as well. Wastewater facilities have reported a higher level of organic load with persistent chemicals due to the increased use of sanitizers, disinfectants, and antibiotics. The solid waste generated due to the COVID-19 pandemic was found to increase both qualitatively and quantitatively. This work presents and summarizes the observed environmental effects of COVID-19 as reported in the literature for different countries worldwide. The work provides a distinct overview considering the effects imposed by COVID-19 on the air, water, wastewater, and solid waste as critical elements of the environment.

Urban Ground-Level O3 Trends: Lessons from Portuguese Cities, 2010–2018

Big datasets of air-quality pollutants and weather data allow us to review trends of NO2, NO, O3, and global radiation (GR), for Lisbon, Porto and Coimbra, with regard to the historical period of 2010–2018. GR is expected to have a considerable impact on photochemical reactions of the O3 formation mechanism. We aim to characterize daily, monthly, and yearly trends. We explore Weekday (WD) versus weekend (WE), and seasonality of O3 and NO2. We are interested to know these pollutant peak concentration variations over the years and investigate if parallels can be drawn between urban mobility indicators and these pollutants. For this purpose, economic data, European emission standards, and car stock data (fuel, age, and number of vehicles) are cross-analyzed. How are they correlated? Has it impacted NO2 and O3 variations? How do different air-quality monitoring stations (AQMS), traffic and non-traffic, compare? How is Lisbon NOx-O3 correlated? What are its implications for future scenarios? Results show that urban mobility trends and economic events are correlated with NO2 and O3 variability. Weekend effect has a partial relationship with urban mobility trends and economy as it is relatively well correlated for Lisbon but not for Porto and Coimbra. Nonetheless, weekend effect for the period of 2010–2018 is overall trending upwards for all cities. In Lisbon and Coimbra, O3 concentrations also trend upwards during the same 2010–2018 period but for Porto they do not. Regardless, for the period of 2015–2018, after the economic recession, the upwards trends of both weekend effect and overall O3 concentrations are clear for all AQMS. For AQMS peak values comparison, Lisbon traffic AQMS registered an annual averaged 8-hour daily max O3 concentration of 34.4 ppb while Lisbon non-traffic AQMS presented 39.1 ppb. Altogether, annual 8-hour daily maximum values for 2010–2018 traffic AQMS in Lisbon show an inverse relationship with fuel sales, and have concentrations fluctuating between 28–35 ppb, which is slightly higher than the 2001–2010 historical European range of 27–31 ppb. Lastly, for the 8 years data in Lisbon, it has been shown that a negative NOx-O3 correlation exists, and the study location might be VOC–sensitive. This means that as NOx concentrations decrease, O3 concentrations become exponentially higher. Further research into VOCs with better data availability is required to make more concise claims. Regardless, it can be inferred that in a future scenario where mitigation continues to escalate, through O3 emission standards and an aggressive shift of car stock to electric vehicles, achieving unprecedented rises in O3 concentrations could be observed.