The stock market and NO2 emissions effects of COVID‐19 around the world

In this paper, we study the impact of the coronavirus disease 2019 pandemic in estimated panel vector autoregression models for 92 countries. The large cross‐section of countries allows us to shed light on the heterogeneity of the responses of stock markets and nitrogen dioxide emissions as high‐frequency measures of economic activity. We quantify the effect of the number of infections and four dimensions of policy measures: (1) containment and closure, (2) movement restrictions, (3) economic support, and (4) adjustments of health systems. Our main findings show that a surprise increase in the number of infections triggers a drop in our two measures of economic activity. Propping up economic support measures, in contrast, raises stock returns and emissions and, thus, contributes to the economic recovery. We also document vast differences in the responses across subsets of countries and between the first and the second wave of infections.

"Fortune amidst misfortune”: The impact of Covid-19 city lockdowns on air quality

Air pollution is one of the major causes of health risks as it leads to widespread disease and death each year. Countries have invested heavily in fighting air pollution, arguably without convincing results. The outbreak of the highly infectious disease COVID-19 in December 2019 has been declared a pandemic and a worldwide health crisis by World Health Organization (WHO). Countries resorted to city lockdowns that sternly curtailed personal mobility and economic activities to control the spread of this deadly coronavirus disease. This paper examines the impact of Covid-19 city lockdowns on air quality. The researchers adopted a comprehensive interpretative document analysis for this study, which guided the careful but rigorous examination of air quality and coronavirus data. This method affirmed the authenticity of the information examined and interpreted in the US, Italy and China, the study areas. The study found that Covid-19 city lockdowns have contributed to a significant improvement in air quality within the first four months of the outbreak of Covid-19. National Aeronautics and Space Administration (NASA) had reported that NO2 concentrations in the study areas had reduced significantly using evidence from their Sentinel-5P instrument. Air quality in Covid-19 cities' lockdowns also improved because of the enforcement of other types of measures enacted to battle the virus. WHO still believes that the amount of NO2 concentration in the atmosphere is still high per their standards and regulations. Based on this, the researchers recommend that governments and other stakeholders put in much effort in terms of legislation to "win the war” against air pollution.

Mortality burden due to ambient nitrogen dioxide pollution in China: Application of high-resolution models.

BACKGROUND: A large gap exists between the latest Global Air Quality Guidelines (AQG 2021) and Chinese air quality standards for NO2. Assessing whether and to what extent air quality standards for NO2 should be tightened in China requires a comprehensive understanding of the spatiotemporal characteristics of population exposure to ambient NO2 and related health risks, which have not been studied to date. OBJECTIVE: We predicted ground NO2 concentrations with high resolution in mainland China, explored exposure characteristics to NO2 pollution, and assessed the mortality burden attributable to NO2 exposure. METHODS: Daily NO2 concentrations in 2019 were predicted at 1-km spatial resolution in mainland China using random forest models incorporating multiple predictors. From these high-resolution predictions, we explored the spatiotemporal distribution of NO2, population and area percentages with NO2 exposure exceeding criterion levels, and premature deaths attributable to long- and short-term NO2 exposure in China. RESULTS: The cross-validation R2and root mean squared error of the NO2 predicting model were 0.80 and 7.78 µg/m3, respectively,at the daily level in 2019.The percentage of people (population number) with annual NO2 exposure over 40 µg/m3 in mainland China in 2019 was 10.40 % (145,605,200), and it reached 99.68 % (1,395,569,840) with the AQG guideline value of 10 µg/m3. NO2 levels and population exposure risk were elevated in urban areas than in rural. Long- and short-term exposures to NO2 were associated with 285,036 and 121,263 non-accidental deaths, respectively, in China in 2019. Tightening standards in steps gradually would increase the potential health benefit. CONCLUSION: In China, NO2 pollution is associated with significant mortality burden. Spatial disparities exist in NO2 pollution and exposure risks. China's current air quality standards may no longer objectively reflect the severity of NO2 pollution and exposure risk. Tightening the national standards for NO2 is needed and will lead to significant health benefits.

Changes in Tropospheric Nitrogen Dioxide Vertical Column Densities over Japan and Korea during the COVID-19 Using Pandora and MAX-DOAS

We investigated the impact of human activity during COVID-19 on the tropospheric nitrogen dioxide vertical column density (NO2 TropVCD) at three urban sites (Gwangju and Busan in Korea and Yokosuka in Japan) and one remote site (Cape Hedo in Japan) from Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) and Pandora. Compared to the monthly mean NO2 TropVCD from 2015 to 2018 and in 2019, the values were lower in 2020 due to social distancing in Korea and Japan. High negative relative changes were observed from May to September (-30% to -18%) at the three urban sites;Cape Hedo, a remote site, did not show a significant difference in relative changes between previous years and 2020, suggesting that only anthropogenic emission sources decreased dramatically. In the case of Yokosuka, the 15-day moving average of the NO2 TropVCD exhibited a good relationship with transportation (R = 0.48) and industry (R = 0.54) mobility data. In contrast, the NO2 TropVCD at the Korean sites showed a moderate to low correlation with the industrial sector and insignificant correlations with transportation. The differences in correlations might be caused by the different social distancing policies in Korea (voluntary) and Japan (mandatory). By applying generalized boosted models to exclude meteorological and seasonal effects associated with NO2 TropVCD variations, we revealed that the decreasing trend from 2019 to 2020 was much steeper than that from 2015 to 2020 (a factor of two), and a significant change was identified in January 2020, when the first cases of COVID-19 were observed in both Korea and Japan. This result confirmed that the reduction in NO2 can be largely explained by the NOx emission reduction resulting from social distancing for COVID-19 rather than annual meteorological differences;however, in December 2020, NO2 recovered suddenly to its previous level due to an increase in human activities.

Air quality changes in Shijiazhuang during COVID-19 outbreak

[Background] The coronavirus disease 2019 (COVID-19) was first detected in December 2019. To combat the disease, a series of strict measures were adopted across the country, which led of improved air quality. This provides an opportunity to discuss the impact of human activities on air quality. [Objective] This study investigates the air quality changes in Shijiazhuang, and analyzes the impacts of epidemic prevention and control measures on air quality, so as to provide reference and ideas for further improving air quality and prevention and control measures. [Methods] The air quality data were collected online from https://www.zq12369.com/ and https://aqicn.org/city/shijiazhuang/cn/. Comparisons in air quality index (AQI) and the concentrations of air pollutants (PM2.5, PM10, SO2, CO, NO2, and O3) were made between the period from December 2019 to June 2020 (reference) and the same period from 2016 to 2019 by t-test and chi-square test. [Results] The daily average AQI dropped by 25.38% in Shijiazhuang during the COVID-19 prevention and control compared with the some period from 2016 to 2019 (t=6.28, P < 0.05). The proportions of pollution days during the COVID-19 outbreak in Shijiazhuang were PM2.5 (44.56%), O3 (31.09%), PM10 (23.83%), and NO2 (2.59%) successively, the pollution days of PM10 decreased significantly (chi2=3.86, P < 0.05) compared with 2016-2019, but during traffic lockdown the numbers of pollution days of PM2.5 and in the mid stage of prevention the number of pollution days of O3 increased (P < 0.05). Compared with the control period, the concentrations of the six air pollutants decreased to varying degrees (P < 0.05), especially SO2 dropped by 55.36%. [Conclusion] The measures taken for COVID-19 control and prevention have reduced the pollution sources and emissions, which resulted in better general air quality of Shijiazhuang City, but have aggravated the pollution of O3 and other pollutants. It is necessary to further explore the causes for the aggravation of O3 pollution in order to formulate reasonable air quality control strategies.Copyright © 2021, Shanghai Municipal Center for Disease Control and Prevention. All rights reserved.

Association between nitrogen dioxide concentration and hypertension hospitalization: A multicenter time-series analysis in Xinxiang, China

Hypertension is a common chronic disease, and air pollution is strongly associated with hypertension hospitalization. However, the association between nitrogen dioxide (NO2)1 concentration and hypertension hospitalization has rarely been studied. We collected daily data on hypertension hospitalizations, air pollutants, and meteorology from January 1, 2016 to October 31, 2021. After controlling for the effects of seasonal and long-term trends, weather conditions, weekdays, holidays, and during the novel coronavirus crown epidemic, a generalized additive model with over discrete Poisson regression was used to simulate the association between NO2 concentration and hypertension hospitalizations while quantifying hypertension hospitalizations, hospital stays, and hospital costs attributable to NO2. The results showed that each 10 μg/m3 increase in NO2 concentration was significantly associated with the relative risk (RR) of hypertension admission in Xinxiang, with the greatest effect at lag04 (RR = 1.107;95% confidence interval, 1.046–1.172). Hypertension hospitalizations attributed to NO2 during the study period accounted for 9.70% (484) of the total hypertension hospitalizations, equivalent to 4202 hospital days and 338.55 thousand United States dollars (USD). Increased NO2 concentration increases the risk of hypertension hospitalization in Xinxiang, which poses a significant health and economic burden to society and patients. The findings of this study provide a basis for developing stricter environmental pollutant standards.

Atmospheric pollution during the Covid-19 pandemics in Slovakia

The changes in the particle pollution in eight Slovak regions were evaluated by a paired t-test to verify the significance of the concn. changes during, before and after the lockdowns introduced in 2020 as compared with those of 2019. The obsd. air quality improvement was not flat-rate and rather momentary, and it did not last even through the whole restriction period. Significant decreases were recorded only for NO 2 concn. The European Commission limits of particulate pollution were exceeded in Slovakia. A concise literature rev. was also included.

Effect of vegetation and land surface temperature on NO2 concentration: A Google Earth Engine-based remote sensing approach

With the increasing tension on the global sustainable environment in the urban areas, it is essential to monitor the airborne pollutants and understand the underlying factors that can trigger the situation in a worst-case scenario. Because of its cramped living conditions, excessive coal and fuel usage, and rapid deforestation, the southeast Asian region has historically had worse air quality than the rest of the world. The economic hubs of India and Bangladesh, in particular, have drawn so much attention away from rural regions that unrestrained urbanization is becoming controversial for planners, engineers, and stakeholders in sustainable development. This research combines the two main Asian capital regions, Delhi and Dhaka. It analyzes the change in nitrogen dioxide (NO2) concentration, land surface temperature (LST), and vegetation dynamics across three years (2019-2021) for summer and winter. The NO2 concentration data from Sentinel-5P has been extracted using Google Earth Engine (GEE), and Landsat-8 imagery was utilized for LST, Normalizer Vegetation Index (NDVI), and Enhance Vegetation Index (EVI). The statistical analysis has been carried out by dividing the research regions into one sq. km grid (1512 grids for Delhi and 1485 grids for Dhaka). According to descriptive research, Dhaka's condition is worse than Delhi's, with significant vegetation loss with LST and NO2 concentrations rising. In both research regions, the NO2 concentration is high throughout the winter. The Pearson correlation value demonstrates a negative association between total NO2 concentration and mean NDVI and EVI values and a positive relationship between total NO2 concentration and mean LST. The data have been further assessed using linear regression, which overlaps the correlation result with a maximum R-squared value of 0.2998 for NO2 and EVI in winter 2019.

Impact of Covid-19 Pandemic on Atmospheric Environment over China: A Satellite Perspective

The Coronavirus Disease 2019 (COVID-19) pandemic, which has lasted for more than two years, has had a huge impact on human health and the global economy, as well as the ecological environment. In this study, the variations of atmospheric environment over China from 2019 to 2020 were calculated and analyzed based on the measured total columns of ozone (O-3), sulfur dioxide (SO2), nitrogen dioxide (NO2) and aerosol optical depth (AOD) from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura satellite. The study shows the impact of the epidemic prevention and control measures and the resumption of work and production on atmospheric environment, and demonstrates that satellite remote sensing can play an important role in the monitoring of the COVID-19 pandemic, especially its impact on atmospheric environment.

Effects of COVID‐19 lockdown measures on nitrogen dioxide and black carbon concentrations close to a major Italian motorway

During the first half of 2020, the Italian government imposed several restrictions to limit the spread of the COVID‐19 pandemic: at the beginning of March, a heavy lockdown regime was introduced leading to a drastic reduction of traffic and, consequently, traffic‐related emissions. The aim of this study is to evaluate the effects of these restrictions on pollutant concentrations close to a stretch of the Italian A22 motorway lying in the Alpine Adige valley. In particular, the analysis focuses on measured concentrations of nitrogen dioxide (NO2) and black carbon (BC). Results show that, close to the motorway, NO2 concentrations dropped by around 45% during the lockdown period with respect to the same time period of the previous 3 years. The equivalent analysis for BC shows that the component related to biomass burning, mostly due to domestic heating, was not particularly affected by the restrictions, while the BC component related to fossil fuels, directly connected to traffic, plummeted by almost 60% with respect to the previous years. Since atmospheric concentrations of pollutants depend both on emissions and meteorological conditions, which can mask the variations in the emission regime, a random forest algorithm is also applied to the measured concentrations, in order to better evaluate the effects of the restrictions on emissions. This procedure allows for obtaining business‐as‐usual and meteorologically normalized time series of both NO2 and BC concentrations. The results derived from the random forest algorithm clearly confirm the drop in NO2 emissions at the beginning of the lockdown period, followed by a slow and partial recovery in the following months. They also confirm that, during the lockdown, emissions of the BC component due to biomass burning were not significantly affected, while those of the BC component related to fossil fuels underwent an abrupt drop.

Impact of Anthropogenic Emission Reduction during COVID-19 on Air Quality in Nanjing, China

To avoid the spread of COVID-19, China has implemented strict lockdown policies and control measures, resulting in a dramatic decrease in air pollution and improved air quality. In this study, the air quality model WRF-Chem and the latest MEIC2019 and MEIC2020 anthropogenic emission inventories were used to simulate the air quality during the COVID-19 lockdown in 2020 and the same period in 2019. By designing different emission scenarios, this study explored the impact of the COVID-19 lockdown on the concentration of air pollutants emitted by different sectors (industrial sector and transportation sector) in Nanjing for the first time. The results indicate that influenced by the COVID-19 lockdown policies, compared with the same period in 2019, the concentrations of PM2.5, PM10, and NO2 in Nanjing decreased by 15%, 17.1%, and 20.3%, respectively, while the concentration of O3 increased by 45.1% in comparison;the concentrations of PM2.5, PM10 and NO2 emitted by industrial sector decreased by 30.7%, 30.8% and 14.0% respectively;the concentrations of PM2.5, PM10 and NO2 emitted by transportation sector decreased by 15.6%, 15.7% and 26.2% respectively. The COVID-19 lockdown has a greater impact on the concentrations of PM2.5 and PM10 emitted by the industrial sector, while the impact on air pollutants emitted by the transportation sector is more reflected in the concentration of NO2. This study provides some theoretical basis for the treatment of air pollutants in different departments in Nanjing.

Estimation of NO2 emission strengths over Riyadh and Madrid from space from a combination of wind-assigned anomalies and a machine learning technique

Nitrogen dioxide (NO2) air pollution provides valuable information for quantifying NOx (NOx = NO + NO2) emissions and exposures. This study presents a comprehensive method to estimate average tropospheric NO2 emission strengths derived from 4-year (May 2018–June 2022) TROPOspheric Monitoring Instrument (TROPOMI) observations by combining a wind-assigned anomaly approach and a machine learning (ML) method, the so-called gradient descent algorithm. This combined approach is firstly applied to the Saudi Arabian capital city of Riyadh, as a test site, and yields a total emission rate of 1.09×1026 molec. s-1. The ML-trained anomalies fit very well with the wind-assigned anomalies, with an R2 value of 1.0 and a slope of 0.99. Hotspots of NO2 emissions are apparent at several sites: over a cement plant and power plants as well as over areas along highways. Using the same approach, an emission rate of 1.99×1025 molec. s-1 is estimated in the Madrid metropolitan area, Spain. Both the estimate and spatial pattern are comparable with the Copernicus Atmosphere Monitoring Service (CAMS) inventory.Weekly variations in NO2 emission are highly related to anthropogenic activities, such as the transport sector. The NO2 emissions were reduced by 16 % at weekends in Riyadh, and high reductions were found near the city center and in areas along the highway. An average weekend reduction estimate of 28 % was found in Madrid. The regions with dominant sources are located in the east of Madrid, where residential areas and the Madrid-Barajas airport are located. Additionally, due to the COVID-19 lockdowns, the NO2 emissions decreased by 21 % in March–June 2020 in Riyadh compared with the same period in 2019. A much higher reduction (62 %) is estimated for Madrid, where a very strict lockdown policy was implemented. The high emission strengths during lockdown only persist in the residential areas, and they cover smaller areas on weekdays compared with weekends. The spatial patterns of NO2 emission strengths during lockdown are similar to those observed at weekends in both cities. Although our analysis is limited to two cities as test examples, the method has proven to provide reliable and consistent results. It is expected to be suitable for other trace gases and other target regions. However, it might become challenging in some areas with complicated emission sources and topography, and specific NO2 decay times in different regions and seasons should be taken into account. These impacting factors should be considered in the future model to further reduce the uncertainty budget.

Does environmental quality and weather induce COVID-19: Case study of Istanbul, Turkey

The coronavirus pandemic has infected more than 100 million people worldwide with COVID-19, with millions of deaths across the globe. In this research, we explored the effects of environmental and weather variables with daily COVID-19 cases and COVID-19 fatalities in Istanbul, Turkey. Turkey has the 8th highest number of COVID-19 cases globally, with the highest infections and deaths in Istanbul. This may be the first study to conduct a comprehensive investigation for environmental quality (air quality pollutants, e.g., PM2.5 and PM10, ozone, nitrogen dioxide, sulfur dioxide, carbon monoxide, etc.), weather parameters (temperature, humidity) and COVID-19 in Turkey. The authors collected meteorological data from 11 March 2020 to 8 February 2021 and COVID-19 data from Istanbul and other regions. The results from empirical estimations, correlation analysis, and quantile on quantile techniques support that air quality and temperature significantly influence COVID-19 deaths in Istanbul. This research may help policymakers and health scientists to take specific measures to reduce the spread of coronavirus across different global cities.The effects of air quality on COVID-19 in Istanbul was investigated.The study applied correlation and quantile on quantile techniques over daily data.Temperature significantly induces the spread of COVID-19 in Istanbul at all quantiles.Air quality and Nitrogen are positively linked with COVID-19 new cases.

Role of the COVID-19 imposed lockdown in climate change

As the transmission of COVID-19 increases rapidly, the whole world adopted the lockdown activity with restriction of human mobility to prevent its spread. Everyone thinks of the COVID-19 negatively;however, it has some positive aspects too. Before COVID-19, the world was suffering by a high level of urban air pollution especially in the form of CO2, SO2, NO2 and particulate matter. During the COVID-19 pandemic, lockdown and limited human engagement with nature accompanied by social distance have proven to be beneficial for nature. As a result, significant reduction in environmental pollution and improvement in the quality of air, cleaner rivers, less noise pollution, undisturbed and calm wildlife was observed. Knowledge gained from the studies suggests that a substantial relationship exists between the contingency measures and environmental health. It is concluded that the COVID-19-induced lockdown has a positive impact on the global warming, a major issue of the 21st century.

Global Atmospheric Composition Needs from Future Ultraviolet–Visible–Near-Infrared (UV–Vis–NIR) NOAA Satellite Instruments

Presentations spanned a range of applications: the public health impacts of poor air quality and environmental justice;greenhouse gas measuring, monitoring, reporting, and verification (GHG MMRV);stratospheric ozone monitoring;and various applications of satellite observations to improve models, including data assimilation in global Earth system models. The combination of methane (CH4), carbon dioxide (CO2), carbon monoxide (CO), and NO2 retrievals can improve confidence in emissions inventories and model performance, and together these data products would be of use in future air quality management tools. The ability to retrieve additional trace gases (e.g., ethane, isoprene, and ammonia) in the thermal IR along with those measured in the UV–Vis–NIR region would be extremely useful for air quality applications, including source apportionment analysis (e.g., for oil/natural gas extraction, biogenic, and agricultural sources). Ground-level ozone is one of six criteria pollutants for which the EPA sets National Ambient Air Quality Standards (NAAQS) to protect against human health and welfare effects.

Photocatalytic-treated asphalt road in Copenhagen for urban NO x removal.

Atmospheric nitrogen oxides ( NO x = NO + NO 2 ) are key pollutants and short-lived climate forcers contributing to acid rain, photochemical smog, aerosol formation and climate change. Exposure to nitrogen dioxide ( NO 2 ) emitted mainly from transportation, causes adverse health effects associated with respiratory illnesses and increased mortality even at low concentration. Application of titanium dioxide ( TiO 2 )-based photocatalysis in urban environment is a new air cleaning solution, activated by sunlight and water vapour to produce OH radicals, able to remove NO x and other pollutants from the planetary boundary layer. This study is a large-scale evaluation of NO x removal efficiency at a near-road environment with applied photocatalytic NOxOFF™ technology on an urban road west of Copenhagen, thus supporting local municipality in meeting their clean-air Agenda 2030. The photocatalytic NOxOFF™ granulate containing TiO 2 nanoparticles was applied on an asphalt road in July 2020 and ambient NO x was measured during a six-month monitoring campaign. It is the first NO x monitoring campaign carried out at this road and specific efforts have been devoted to evaluate the reduction in ambient NO x levels with NOxOFF™-treated asphalt. Several methods were used to evaluate the photocatalytic effect, taking into account analysis limitations such as the short reference period prior to application and the highly uncertain measurement period during which SARS-CoV-2 lockdown measures impacted air quality. There was no statistically significant difference in NO x concentrations between the reference period and the photocatalytic active period and NO removal efficiency resulted in - 0.17 (± 1.27). An upper limit removal of 17.5% NO x was estimated using a kinetic tunnel model. While NO 2 comparison with COPERT V street traffic model projection was roughly estimated to decrease by 39% (± 38%), although this estimate is subject to high uncertainty. The observed annual mean NO 2 concentration complies with Frederiksberg clean-air Agenda 2030 and air quality standards. Graphical abstract: A graphical abstract illustrating the air cleaning properties of TiO 2 -based photocatalytic-treated asphalt.

Performance of Bayesian Model Averaging (BMA) for Short-Term Prediction of PM10 Concentration in the Peninsular Malaysia

In preparation for the Fourth Industrial Revolution (IR 4.0) in Malaysia, the government envisions a path to environmental sustainability and an improvement in air quality. Air quality measurements were initiated in different backgrounds including urban, suburban, industrial and rural to detect any significant changes in air quality parameters. Due to the dynamic nature of the weather, geographical location and anthropogenic sources, many uncertainties must be considered when dealing with air pollution data. In recent years, the Bayesian approach to fitting statistical models has gained more popularity due to its alternative modelling strategy that accounted for uncertainties for all air quality parameters. Therefore, this study aims to evaluate the performance of Bayesian Model Averaging (BMA) in predicting the next-day PM10 concentration in Peninsular Malaysia. A case study utilized seventeen years' worth of air quality monitoring data from nine (9) monitoring stations located in Peninsular Malaysia, using eight air quality parameters, i.e., PM10, NO2, SO2, CO, O3, temperature, relative humidity and wind speed. The performances of the next-day PM10 prediction were calculated using five models' performance evaluators, namely Coefficient of Determination (R2), Index of Agreement (IA), Kling-Gupta efficiency (KGE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The BMA models indicate that relative humidity, wind speed and PM10 contributed the most to the prediction model for the majority of stations with (R2 = 0.752 at Pasir Gudang monitoring station), (R2 = 0.749 at Larkin monitoring station), (R2 = 0.703 at Kota Bharu monitoring station), (R2 = 0.696 at Kangar monitoring station) and (R2 = 0.692 at Jerantut monitoring station), respectively. Furthermore, the BMA models demonstrated a good prediction model performance, with IA ranging from 0.84 to 0.91, R2 ranging from 0.64 to 0.75 and KGE ranging from 0.61 to 0.74 for all monitoring stations. According to the results of the investigation, BMA should be utilised in research and forecasting operations pertaining to environmental issues such as air pollution. From this study, BMA is recommended as one of the prediction tools for forecasting air pollution concentration, especially particulate matter level.

Changes of Air Pollutants in Urban Cities during the COVID-19 Lockdown-Sri Lanka

In response to the COVID-19 pandemic in early 2020, Sri Lanka underwent a nationwide lockdown that limited motor vehicle movement, industrial operations, and human activities. This study analyzes the impact of COVID-19 lockdown on carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM10, PM2.5) concentrations in two urban cities (Colombo and Kandy) in Sri Lanka, by comparison of data from the lockdown period (March to May 2020) with its analogous period of 2019 and 2021. The results showed that the percentage change of daytime PM10, PM2.5, CO, and NO2 concentration during the lockdown in Colombo (Kandy) is –42.3% (–39.5%), –46% (–54.2%), –14.7% (–8.8%) and –82.2% (–80.9%), respectively. In both cities, the response of NO2 to the lockdown was the most sensitive. In contrast, daytime O3 concentration in Colombo (Kandy) has increased by 6.7% (27.2%), suggesting that the increase in O3 concentration was mainly due to a reduction in NOx emissions leading to lower O3 titration by NO. In addition, daytime SO2 concentration in Colombo has increased by 22.9%, while daytime SO2 concentration in Kandy has decreased by –40%. During the lockdown period, human activities were significantly reduced, causing significant reductions in industrial operations and transportation activities, further reducing emissions and improving air quality in two cities. The results of this study offer potential for local authorities to better understand the emission sources, assess the effectiveness of current air pollution control strategies, and form a basis for formulating better environmental policies to improve air quality and human health. © The Author(s).

Potential of TROPOMI for understanding spatio-temporal variations in surface NO2 and their dependencies upon land use over the Iberian Peninsula

In orbit since late 2017, the Tropospheric Monitoring Instrument (TROPOMI) is offering new outstanding opportunities for better understanding the emission and fate of nitrogen dioxide (NO2) pollution in the troposphere. In this study, we provide a comprehensive analysis of the spatio-temporal variability of TROPOMI NO2 tropospheric columns (TrC-NO2) over the Iberian Peninsula during 2018–2021, considering the recently developed Product Algorithm Laboratory (PAL) product. We complement our analysis with estimates of NOx anthropogenic and natural soil emissions. Closely related to cloud cover, the data availability of TROPOMI observations ranges from 30 %–45 % during April and November to 70 %–80 % during summertime, with strong variations between northern and southern Spain. Strongest TrC-NO2 hotspots are located over Madrid and Barcelona, while TrC-NO2 enhancements are also observed along international maritime routes close the strait of Gibraltar, and to a lesser extent along specific major highways. TROPOMI TrC-NO2 appear reasonably well correlated with collocated surface NO2 mixing ratios, with correlations around 0.7–0.8 depending on the averaging time.We investigate the changes of weekly and monthly variability of TROPOMI TrC-NO2 depending on the urban cover fraction. Weekly profiles show a reduction of TrC-NO2 during the weekend ranging from -10 % to -40 % from least to most urbanized areas, in reasonable agreement with surface NO2. In the largest agglomerations like Madrid or Barcelona, this weekend effect peaks not in the city center but in specific suburban areas/cities, suggesting a larger relative contribution of commuting to total NOx anthropogenic emissions. The TROPOMI TrC-NO2 monthly variability also strongly varies with the level of urbanization, with monthly differences relative to annual mean ranging from -40 % in summer to +60 % in winter in the most urbanized areas, and from -10 % to +20 % in the least urbanized areas. When focusing on agricultural areas, TROPOMI observations depict an enhancement in June–July that could come from natural soil NO emissions. Some specific analysis of surface NO2 observations in Madrid show that the relatively sharp NO2 minimum used to occur in August (drop of road transport during holidays) has now evolved into a much broader minimum partly de-coupled from the observed local road traffic counting;this change started in 2018, thus before the COVID-19 outbreak. Over 2019–2021, a reasonable consistency of the inter-annual variability of NO2 is also found between both datasets.Our study illustrates the strong potential of TROPOMI TrC-NO2 observations for complementing the existing surface NO2 monitoring stations, especially in the poorly covered rural and maritime areas where NOx can play a key role, notably for the production of tropospheric O3.

Analysis of Volatile Organic Compounds in Exhaled Breath of COVID-19 Patients

Background: More than 2 years since COVID-19's first cases were reported in 2019. Diagnosis of COVID-19 is a key to controlling the pandemic. Sample for COVID-19 testing is collected by naso-oro-pharyngeal swab. This procedure is often uncomfortable and requires a trained examiner. Exhaled breath contains thousands of volatile organic compounds (VOC) which are likely to change during infection. Aims and objectives: This study aims to analyze the difference of VOC in the exhaled breath between COVID-19 and healthy subjects. Method(s): A cross-sectional study was carried out recruiting 90 confirmed cases of COVID-19 and 42 healthy subjects. A sample of exhaled breath was collected by using a 500 ml airbag in both groups. Contained VOC was analyzed using an arrayed sensor breath analyzer to quantify the concentration of CO2, C7H8, C6H14, CH2O, NH4, TVOC, NO2, PM1.0, CO, NH3and Acetone. Statistical analysis was conducted using Mann whitney test. Result(s): The median of CO2, C6H14, NH4, TVOC, NO2, and Acetone are significantly higher in COVID-19 patients compared to healthy subjects (respectively 1175.1 vs 607.3, 0.47 vs 0.0, 1.05 vs 0.0, 146.6 vs 0.05, 1.55 vs 0.04, and 0.23 vs 0.0) while C7H8, CH2O, PM1.0, CO, and NH3are significantly lower (respectively 0.0 vs 0.92, 0.01 vs 0.55, 0.0 vs 4.13, 0.0 vs 0.24, and 0.67 vs 1.99;all with p-value of <0.05.). Furthermore, we found NH4, Acetone, NH3, and CO are positively correlate with severity of COVID-19. Conclusion(s): COVID-19 patients emit distinctive VOC profiles in comparison with healthy subjects.