Tropospheric NO2 Concentrations Research Articles

Satellite-Based Estimation of Near-Surface NO2 Concentration in Cloudy and Rainy Areas

Satellite-based remote sensing enables the quantification of tropospheric NO2 concentrations, offering insights into their environmental and health impacts. However, remote sensing measurements are often impeded by extensive cloud cover and precipitation. The scarcity of valid NO2 observations in such meteorological conditions increases data gaps and thus hinders accurate characterization and variability of concentration across geographical regions. This study utilizes the Empirical Orthogonal Function interpolation in conjunction with the Extreme Gradient Boosting (XGBoost) algorithm and dense urban atmospheric observed station data to reconstruct continuous daily tropospheric NO2 column concentration data in cloudy and rainy areas and thereby improve the accuracy of NO2 concentration mapping in meteorologically obscured regions. Using Chengdu City as a case study, multiple datasets from satellite observations (TROPOspheric Monitoring Instrument, TROPOMI), near-surface NO2 measurements, meteorology, and ancillary data are leveraged to train models. The results showed that the integration of reconstructed satellite observations with provincial and municipal control surface measurements enables the XGBoost model to achieve heightened predictive accuracy (R2 = 0.87) and precision (RMSE = 5.36 μg/m3). Spatially, this approach effectively mitigates the problem of missing values in estimation results due to absent satellite data while simultaneously ensuring increased consistency with ground monitoring station data, yielding images with more continuous and refined details. These results underscore the potential for reconstructing satellite remote sensing information and combining it with dense ground observations to greatly improve NO2 mapping in cloudy and rainy areas.

“Investigating and mapping day-night urban heat island and its driving factors using Sentinel/MODIS data and Google Earth Engine. Case study: Greater Cairo, Egypt”

Urban heat islands (UHI) represent one of the substantial human-induced challenges endangering urban livelihoods. UHI and climate change have significant interactions. Progressively rising warming tendencies are exacerbating higher temperatures in UHI regions. Using time-series data, we examined annual and seasonal day-night UHI intensity and its driving factors in Greater Cairo (GC), which is considered Egypt's megacity, between April 2021 and March 2022. Since Sentinel-1 synthetic aperture radar (SAR) proposes unique horizons for mapping urban areas, we used SAR data and the Random Forest (RF) algorithm for urban footprint extraction in the search region. Sentinel-3 SLSTR thermal images were utilized to derive annual and seasonal day-night land surface temperatures (LST) during the research period. A spatio-temporal assessment of tropospheric NO2, SO2, CO, and aerosol concentrations was performed via the Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI), MODIS, and Google Earth Engine (GEE) applications. The annual average UHI intensity was higher in Egypt's megacity during the nighttime (3.27 K) than during the daytime (0.50 K). This finding indicates that driving forces are boosting temperatures in urban regions at night. The seasonal average day-night UHI intensities have the highest values during the winter, followed by the summer. Significant influencing factors concerning UHI intensity varied between the daytime and nighttime. Moreover, NDBI, PV, NDVI, altitude, and wind speed were the driving factors that were influential during the day and at night. These results can be used to establish effective adaptation measures for combating the impacts of climatic change and UHI.

The spatial dynamics of Ukraine air quality impacted by the war and pandemic

ABSTRACT In recent years, our world has experienced significant disruptions due to the COVID-19 pandemic, and Russia's 2022 invasion of Ukraine, impacting human activities and the global environment. This paper explored air quality changes in Ukraine due to COVID-19, and Russia's invasion of Ukraine using on-demand with a what-you-see-is-what-you-get approach. During the COVID-19 pandemic, strict quarantine policies in Ukraine led to a 2% reduction in tropospheric NO2 concentration before the lockdown and 4% during the lockdown period. Cities like Kyiv, Donetsk, and Dnipro exhibited reductions of 5%, 11%, and 16%, respectively. Total SO2 column concentration decreased by 6% before the lockdown and 2.5% during the lockdown period, except in high population density areas. Kyiv showed the highest reduction of 17% in SO2 concentration, while Donetsk and Dnipro exhibited an 11% reduction. However, during the Russian invasion, there was a significant increase in tropospheric NO2 concentration in heavily destroyed Kharkiv while most eastern regions experienced a reduction. The total SO2 column was 48% higher before the war but reduced throughout the country after the war, except for in Kyiv and a few central regions. These findings can contribute to analyzing air pollution and building digital twin simulations for future reconstruction scenarios.

Sentinel satellite data monitoring of air pollutants with interpolation methods in Guayaquil, Ecuador

In Ecuador, there is a limitation on air quality monitoring due to the cost of monitoring networks. Although air quality monitoring stations are instruments for air measurement, they do not cover an entire city due to their scope. Satellite remote sensing is now an effective tool to study atmospheric pollutants and has been applied to continuously assess a region and overcome the limitations of fixed stations. Despite the application of satellite data for air quality monitoring, there are some limitations, such as measurement frequency, cloud cover and wide spatial resolution, which do not allow the assessment of air pollution in cities. Therefore, downscaling, applying interpolation methods, is essential for continuous air quality monitoring at smaller scales. For this research, Nitrogen Dioxide (NO2) data from the Sentinel-5 satellite percussor was used in the city of Guayaquil for January–December 2020, which is considered before, during and after the COVID-19 quarantine. This mid-size port city does not have a permanent monitoring network, which prevents us from knowing the air quality. Due to the limitation of pixel size, this study used satellite data to apply interpolation techniques and reduce pixels to assess air quality. Two categories of interpolation were selected: deterministic and stochastic. The empirical Bayesian kriging (EBK) interpolation obtained a R2 of 0.9546, which was superior to the other methods applied. Therefore, the EBK method had the best accuracy for tropospheric NO2 concentration. Finally, the method used in this research can help monitor air quality in cities lacking continuous monitoring networks, as the reduction of the pixel size gives us a better pattern of pollutants.

Full-coverage spatiotemporal estimation of surface ozone over China based on a high-efficiency deep learning model

Ozone concentration Monitoring is essential to atmospheric pollution prevention and control. Against the background of severe ozone pollution over China in recent years, a spatiotemporal contiguous ozone concentration mapping method was developed. We imputed the significant data gaps of the Ozone Monitoring Instrument's tropospheric NO2 content by using an efficient machine learning model named LightGBM. Then, we developed a deep learning model based on three-dimensional Convolutional Neural Network architecture for daily maximum 8 h average ozone concentration estimation over China. With the support of the satellite-retrieved precursor, meteorological data and other ancillary data, our model achieved excellent performance with sample-based 10-fold cross-validation R2 = 0.88. Furthermore, we generated daily maximum 8 h average ozone datasets covering the whole of China from 2016 to 2020. This study presents a novel method for surface ozone modeling, which can provide fundamental data for the study of the ecological changes caused by ozone pollution, such as crop loss, or the harmful effects on humans, such as the increased incidence of respiratory diseases.

Impacts of Certain Meteorological Factors on Atmospheric NO2 Concentrations during COVID-19 Lockdown in 2020 in Wuhan, China

The concentration of nitrogen dioxide (NO2) in the air is one of the important indexes for evaluating air quality. At the beginning of 2020, a COVID-19 outbreak suddenly hit Wuhan, China. To effectively control the epidemic, Wuhan was put under a 76-day lockdown, during which we collected tropospheric column amounts in the atmosphere and NO2 concentrations measured at ground monitoring stations, and we reviewed the ground NO2 concentrations in 2019 and the tropospheric NO2 concentrations between 2012 and 2019. Using the random forest (RF) model, we predicted the impact of the tropospheric NO2 concentration during the lockdown period without the occurrence of the COVID-19 epidemic and analyzed the impact of multiple certain meteorological factors on tropospheric and ground NO2 concentrations. The results showed that the tropospheric and ground NO2 concentrations were reduced by 11.04~53.36% and 21.96~65.04%, respectively. The main factors affecting the tropospheric NO2 concentration were wind velocity, land surface temperature, surface lifted index, precipitable water volume and tropospheric relative humanity. The main factors affecting the ground NO2 concentration were tropospheric relative humanity, surface lifted index, land surface temperature and tropospheric temperature. The development of different emission reduction and control measures under different meteorological conditions and the formulation of more refined policies will play positive roles in improving the efficiency of air pollution control.

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.

Spatial variation of tropospheric NO2 concentration during first wave CoVID-19 induced lockdown estimated from Sentinel-5 Precursor

CoVID-19 pandemic has forced many countries to shut down their industries as most of these countries observed complete statewide lockdown during the first wave of this contagious virus. Factories and vehicles on the road are a major source of nitrogen dioxide (NO2 ), which is also one of the major air pollutants. The emission of NO₂ is considered as a good indicator of prevalant global economic activities as its predominant corollary affairs typically involve industrial oprtaions and these emissions are evidently visible from space. Sentinel-5 Precursor (launched in July 2018), equipped with spectrometer Tropomoi (Tropospheric Monitoring Instrument), is a low-earth-orbit atmosphere mission satellite dedicated towards monitoring air pollution sensing remotely. This study is focused primarily on, estimation and variation of NO2 emission over different parts of the world, before and during the first wave of CoVID-19 epidemic (07 weeks duration). It is found that in most of the countries during the lockdown period (7 weeks), air quality was cleaner by about 40 to 50 % over the last year, mainly due to the shutdown of industry and traffic activities. The technology is quite promising and provides a tool to access if a country or place followed complete lockdown.

Air Quality in Two Northern Greek Cities Revealed by Their Tropospheric NO2 Levels

In this article, we aim to show the capabilities, benefits, as well as restrictions, of three different air quality-related information sources, namely the Sentinel-5Precursor TROPOspheric Monitoring Instrument (TROPOMI) space-born observations, the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) ground-based measurements and the LOng Term Ozone Simulation-EURopean Operational Smog (LOTOS-EUROS) chemical transport modelling system simulations. The tropospheric NO2 concentrations between 2018 and 2021 are discussed as air quality indicators for the Greek cities of Thessaloniki and Ioannina. Each dataset was analysed in an autonomous manner and, without disregarding their differences, the common air quality picture that they provide is revealed. All three systems report a clear seasonal pattern, with high NO2 levels during wintertime and lower NO2 levels during summertime, reflecting the importance of photochemistry in the abatement of this air pollutant. The spatial patterns of the NO2 load, obtained by both space-born observations and model simulations, show the undeniable variability of the NO2 load within the urban agglomerations. Furthermore, a clear diurnal variability is clearly identified by the ground-based measurements, as well as a Sunday minimum NO2 load effect, alongside the rest of the sources of air quality information. Within their individual strengths and limitations, the space-borne observations, the ground-based measurements, and the chemical transport modelling simulations demonstrate unequivocally their ability to report on the air quality situation in urban locations.

Spatiotemporal Analysis of NO2 Production Using TROPOMI Time-Series Images and Google Earth Engine in a Middle Eastern Country

Like many developing countries, Iran faces air pollution, especially in its metropolises and industrial cities. Nitrogen dioxide (NO2) is one of the significant air pollutants; therefore, this study aims to investigate the spatiotemporal variability of NO2 using Tropospheric Monitoring Instrument (TROPOMI) sensor mounted on the Sentinel-5P (S5P) satellite and the Google Earth Engine (GEE) platform over Iran. In addition, we used ground truth data to assess the correlation between data acquired by this sensor and ground stations. The results show that there is a strong correlation between products of the TROPOMI sensor and data provided by the Air Quality Monitoring Organization of Iran. The results also display that the correlation coefficient (R) of NO2 between ground truth data and the TROPOMI sensor varies in the range of 0.4 to 0.92, over three years. Over an annual period (2018 to 2021) and wide area, these data can become valuable points of reference for NO2 monitoring. In addition, this study proved that the tropospheric NO2 concentrations are generally located over the northern part of Iran. According to the time and season, the concentration of the tropospheric NO2 column shows higher values during winter than in the summertime. The results show that a higher concentration of the tropospheric NO2 column is in winter while in some southern and central parts of the country more NO2 concentration can be seen in the summertime. This study indicates that these urban areas are highly polluted, which proves the impact of pollutants such as NO2 on the people living there. In other words, small parts of Iran are classified as high and very highly polluted areas, but these areas are the primary location of air pollution in Iran. We provide a code repository that allows spatiotemporal analysis of NO2 estimation using TROPOMI time-series images within GEE. This method can be applied to other regions of interest for NO2 mapping.

Association between satellite-detected tropospheric nitrogen dioxide and acute respiratory infections in children under age five in Senegal: spatio-temporal analysis

BackgroundThere is growing evidence to suggest that exposure to a high concentration of nitrogen dioxide (NO2) can lead to a higher incidence of Acute Respiratory Infections (ARIs) in children; however, such an association remains understudied in Sub-Saharan Africa due to the limited availability of exposure data. This study explored this association by using the satellite-detected tropospheric NO2 concentrations measured by Sentinel-5 Precursor and ARI symptoms in children under age five collected in the Demographic and Health Survey (DHS) in Senegal.MethodsWe matched the daily tropospheric NO2 exposure with the individual ARI symptoms according to the DHS survey clusters spatially and temporally and conducted a logistic regression analysis to estimate the association of exposure to NO2 with ARI symptoms in two preceding weeks.ResultsWe observed a positive association between exposure to continuous levels of NO2 and ARI symptoms after adjusting for confounders (OR 1.27 per 10 mol/m2, 95% CI: 1.06 – 1.52). When the association was further examined by quartile exposure categories, the 4th quartile category was positively associated with symptoms of ARI after adjusting for confounders (OR 1.71, 95% CI: 1.08—2.69). This suggests that exposure to certain high levels of NO2 is associated with the increased risk of children having symptoms of ARI in Senegal.ConclusionsThis study highlights the need for increased research on the effects of ambient NO2 exposure in Africa as well as the need for more robust, ground-based air monitoring in the region. For a country like Senegal, where more than 90% of the population lives in areas that do not meet the national air quality standards, it is urgently required to implement air pollution prevention efforts to protect children from the health hazards of air pollution.

Associations of air pollution concentrations and energy production dynamics in Pakistan during lockdown.

This study investigated atmospheric changes that occurred due to changes in energy production and consumption before and during the COVID-19 pandemic. We analyzed nitrogen dioxide (NO2), aerosol optical depth (AOD), and rainfall patterns to understand the associated changes in emissions, especially from the power generation sector, before (2018 and 2019) and during the lockdown of 2020 across Pakistan. Regression analysis indicated a strong association between energy production by thermal power plants and tropospheric NO2 concentrations. Notably, a comparison between emission sources showed that the NO2 emissions from a single thermal power plant were equivalent to the emissions from a major city. During the lockdown, we observed a 40% reduction in NO2 emissions from coal-based power plants and a 30% reduction in mega- and major cities compared to the same retro in 2019. We also observed an approximate 25% decrease in AOD in the industrial and energy sectors, although no major decrease was obvious in the cities. Rainfall contributed to reducing the NO2 concentrations during monsoon season across all power plants in Pakistan, whereas it did not significantly correlate with AOD. The findings highlight the need for appropriate management and use of renewable energy in the industrial sector and transportation systems. Future research could estimate the environmental and public health costs linked to pollution originating from thermal energy production and poor transportation infrastructure.

Change In Nitrogen Dioxide (No2 ) Concentration Due To The Lockdown Amid The Covid-19 Pandemic In India

The study aimed to examine the change in the concentration of nitrogen dioxide due to the lockdown amid the COVID-19 pandemic in India at the district level using Sentinel-5P TROPOMI. The spatio-temporal characteristics of the tropospheric column NO2 concentration during 45 days of the lockdown were compared with the same days of 2019. Further, to model spatially varying relationships of NO2 during the lockdown period, it was given as a dependent variable whereas NO2 during the pre-lockdown period was considered as an independent variable. Results show that the mean NO2 concentration was reduced from 0.00406 mol/m2 before the lockdown (2019-03-25 to 2019-05-10) to 0.0036 mol/m2 during the lockdown period (2020-03-25 to 2020-05-10). The maximum decline of NO2 concentration was observed in Gautam Buddha Nagar and Delhi. This indicates the high level of atmospheric pollution due to the excess use of fuel in human activities. The results of the Ordinary Least Squares (OLS) method show a strong positive relationship between both variables. Positive standard residuals indicate that the concentration of NO2 has reduced more than expected as per the OLS model. The z-score (24.11) was obtained from spatial autocorrelation. It indicates that residuals are highly clustered and there is less than a 1% likelihood that this clustered pattern could be a result of a random chance. The highest decrease was observed in districts/urban agglomerations of Gautam Buddha Nagar (-40%), Delhi (-37%), Greater Bombay (-31%), Hyderabad (-29%), Faridabad (-29%), Bangalore Urban (-28%), Gandhinagar (-27%), Chennai (-27%) and Gurgaon (-26%) respectively.

Time series analysis and spatial distribution map of aggregate risk index due to tropospheric NO2 and O3 based on satellite observation

A high increase in human activities has led to more emission of air pollutants in metropolises and industrial areas. Recently, remotely sensed data of tropospheric pollutants is used for environmental management and decision-making on large scale. The purpose of this study was a time series analysis of nitrogen dioxide Vertical Column Density (NO2 VCD) and Ozone (O3) using Ozone Monitoring Instrument (OMI) from 2005 to 2016 by Mann-Kendall test. Also, the aggregate risk index (ARI) was calculated to estimate the overall impact of exposure to tropospheric NO2 and O3 concentrations at the national scale in 2016. To estimate the surface NO2 related drivers, The Radial Basis Function (RBF) neural network modeling was performed for different months of 2016. Results of Mann-Kendall test showed that tropospheric ozone concentration had an increasing trend in all parts of Iran and this increasing trend was significantly higher in the southern region of Iran and lower in the northern parts of Iran. NO2 VCD in most parts of Iran had a significant increasing trend. The result of sensitivity analysis showed that NO2 VCD (1.25), the distance to the industrial area, (1.20) and wind speed (1.07) were the most important variables for the estimation of surface NO2 concentration. Spatial ARI with the highest risks is mainly located in the Northern half of Iran, especially in Tehran, Alborz, and Khorasan-e− Razavi provinces, where NO2 and O3 concentrations are very severe. In northern Iran and central cities, the ARI values are calculated from 1.5 to 2.08, indicating the highest human health risks in these regions. The human health risks based on OMI observation were obtained higher in comparison to AQM data because the satellite data coverage is larger than AQM station and monitors transmitted air pollution by the wind in addition to local pollution. Based on this research, using satellite observation for air quality monitoring is a suitable tool for environmental management on a national scale.

Global assessment of tropospheric and ground air pollutants and its correlation with COVID-19

The declaration of COVID-19 pandemic by the WHO initiated a series of lockdowns globally that varied in stringency and duration; however, the spatiotemporal effects of these lockdowns on air quality remain understudied. This study evaluates the global impact of lockdowns on air pollutants using tropospheric and ground-level indicators over a five-month period. Moreover, the relationship between air pollution and COVID-19 cases and mortalities was examined. Changes in the global tropospheric (NO2, aerosols, and O3) and ground-level (PM2.5, PM10, NO2, and O3) pollutants were observed, and the maximum air quality improvement was observed immediately after lockdown. Except for a few countries, a decline in air pollutants correlated with a reduction in Land Surface Temperature (LST). Notably, regions with higher tropospheric NO2 and aerosol concentrations were also COVID-19 hotspots. Our analysis showed moderate positive correlation for NO2 with COVID-19 cases (R2 = 0.33; r = 0.57, P = 0.006) and mortalities (R2 = 0.40; r = 0.63, P = 0.015), while O3 showed a weak-moderate positive correlation with COVID-19 cases (R2 = 0.22; r = 0.47, P = 0.003) and mortalities (R2 = 0.12; r = 0.35, P = 0.012). However, PM2.5, and PM10 showed no significant correlation with either COVID-19 cases or mortality. This study reveals that humans living under adverse air pollution conditions are at higher risk of COVID-19 infection and mortality.

Spatiotemporal variation of surface ozone and its causes in Beijing, China since 2014

The surface ozone (O3) spatiotemporal distribution, variations and its causes in Beijing in 2014–2020 are revealed by quantitative estimates of the trends of 12 ozone metrics relevant to human health and tropospheric NO2 and formaldehyde (HCHO) column concentration, based on the air quality monitoring network data and satellite retrievals from the Ozone Monitoring Instrument (OMI). Results showed that the maximum O3 concentrations decreased slightly since 2014. The annual 90th percentile of the daily maximum 8-h average (MDA8) O3 and the “ozone-season” (April–September) MDA8 decreased by 0.32 and 0.19 μg m−3 year−1, respectively. The changes in Beijing contrast the widespread and significant O3 increases in the Beijing–Tianjin–Hebei region in recent years. However, significant positive trends in MDA8-90th and ozone-season MDA8 were observed in 7% and 14% of monitoring sites in Beijing (p < 0.05), most of which are located in urban areas and suburbs close to urban areas. The spatial distribution of high O3 values in Beijing has changed significantly. The areas with high O3 concentrations shifted from the northern suburbs (Changping and Miyun districts) to the northeastern and southwestern suburbs and the east of the central urban area. The ozone-season NO2 and HCHO tropospheric columns showed reductions of 4.2% and 0.4% year−1, respectively, from 2014 to 2019 in Beijing, which suggested that the decrease in NOX emissions was much greater than the decrease in volatile organic compound (VOC) emissions. Such an extremely inappropriate control ratio of ozone precursor NOX/VOCs led to an overall trend of slow declining fluctuations of O3 and an increasing trend of individual sites in Beijing. Obviously, the reduction of VOCs in Beijing is far from sufficient, and the significant decrease of Beijing's O3 concentration can only be achieved by a drastically reduction in VOC emissions.

Impacts of partial to complete COVID-19 lockdown on NO2 and PM2.5 levels in major urban cities of Europe and USA

SARS CoV-2 (COVID-19) coronavirus has been causing enormous suffering, death, and economic losses worldwide. There are rigorous containment measures on industries, non-essential business, transportation, and citizen mobility to check the spread. The lockdowns may have an advantageous impact on reducing the atmospheric pollutants. This study has analyzed the change in atmospheric pollutants, based on the Sentinel–5Ps and ground-station observed data during partial to complete lockdown period in 2020. Results revealed that the mean tropospheric NO2 concentration substantially dropped in 2020 due to lockdown against the same period in 2019 by 18–40% over the major urban areas located in Europe (i.e. Madrid, Milan, Paris) and the USA (i.e. New York, Boston, and Springfield). Conversely, urban areas with partial to no lockdown measures (i.e. Warsaw, Pierre, Bismarck, and Lincoln) exhibited a relatively lower dropdown in mean NO2 concentration (3 to 7.5%). The role of meteorological variability was found to be negligible. Nevertheless, the reduced levels of atmospheric pollutants were primarily attributed to the shutdown of vehicles, power plants, and industrial emissions. Improvement in air quality during COVID-19 may be temporary, but regulatory bodies should learn to reduce air pollution on a long-term basis concerning the trade-offs between the environment, society, and economic growth. The intersection of urban design, health, and environment should be addressed by policy-makers to protect public health and sustainable urban policies could be adopted to build urban resilience against any future emergencies.

Improvement in air quality and its impact on land surface temperature in major urban areas across India during the first lockdown of the pandemic

The SARS CoV-2 (COVID-19) pandemic and the enforced lockdown have reduced the use of surface and air transportation. This study investigates the impact of the lockdown restrictions in India on atmospheric composition, using Sentinel–5Ps retrievals of tropospheric NO2 concentration and ground-station measurements of NO2 and PM2.5 between March–May in 2019 and 2020. Detailed analysis of the changes to atmospheric composition are carried out over six major urban areas (i.e. Delhi, Mumbai, Kolkata, Chennai, Bangalore, and Hyderabad) by comparing Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) and land surface temperature (LST) measurements in the lockdown year 2020 and pre-lockdown (2015–2019). Satellite-based data showed that NO2 concentration reduced by 18% (Kolkata), 29% (Hyderabad), 32–34% (Chennai, Mumbai, and Bangalore), and 43% (Delhi). Surface-based concentrations of NO2, PM2.5, and AOD also substantially dropped by 32–74%, 10–42%, and 8–34%, respectively over these major cities during the lockdown period and co-located with the intensity of anthropogenic activity. Only a smaller fraction of the reduction of pollutants was associated with meteorological variability. A substantial negative anomaly was found for LST both in the day (−0.16 °C to −1 °C) and night (−0.63 °C to −2.1 °C) across select all cities, which was also consistent with air temperature measurements. The decreases in LST could be associated with a reduction in pollutants, greenhouse gases and water vapor content. Improvement in air quality with lower urban temperatures due to lockdown may be a temporary effect, but it provides a crucial connection among human activities, air pollution, aerosols, radiative flux, and temperature. The lockdown for a shorter-period showed a significant improvement in environmental quality and provides a strong evidence base for larger scale policy implementation to improve air quality.

Continuous multi-component MAX-DOAS observations for the planetary boundary layer ozone variation analysis at Chiba and Tsukuba, Japan, from 2013 to 2019

Ground-based remote sensing using multi-axis differential optical absorption spectroscopy (MAX-DOAS) was used to conduct continuous simultaneous observations of ozone (O3), nitrogen dioxide (NO2), and formaldehyde (HCHO) concentrations at Chiba (35.63° N, 140.10° E, 21 m a.s.l.) and Tsukuba (36.06° N, 140.13° E, 35 m a.s.l.), Japan, for 7 years from 2013 to 2019. These are urban and suburban sites, respectively, in the greater Tokyo metropolitan area. NO2 and HCHO are considered to be proxies for nitrogen oxides (NOx) and volatile organic compounds (VOCs), respectively, both of which are major precursors of tropospheric O3. The mean concentrations below an altitude of 1 km were analyzed as planetary boundary layer (PBL) concentrations. For a more spatially representative analysis around the urban area of Chiba, four MAX-DOAS instruments directed at four different azimuth directions (north, east, west, and south) were operated simultaneously and their data were unified. During the 7-year period, the satellite observations indicated an abrupt decrease in the tropospheric NO2 concentration over East Asia, including China. This suggested that the transboundary transport of O3 originating from the Asian continent was likely suppressed or almost unchanged during the period. Over this time period, the MAX-DOAS observations revealed the presence of almost-constant annual variations in the PBL O3 concentration, whereas reductions in NO2 and HCHO concentrations occurred at rates of approximately 6–10%/year at Chiba. These changes provided clear observational evidence that a decreasing NOx concentration significantly reduced the amount of O3 quenched through NO titration under VOC-limited conditions in the urban area. Under the dominant VOC-limited conditions, the MAX-DOAS-derived concentration ratio of HCHO/NO2 was found to be below unity in all months. Thus, the multi-component observations from MAX-DOAS provided a unique data set of O3, NO2, and HCHO concentrations for analyzing PBL O3 variations.

On the effect of sea breeze regime on aerosols and gases properties in the urban area of Rome, Italy

Several ground-based remote sensing and in-situ instruments were used to investigate the development of the sea-breeze front and its effect on both the optical and physical aerosol properties, the Particulate Matter (PM) content and the tropospheric and near-surface NO2 concentrations. Most of the instruments belongs to the Boundary-layer Air Quality-analysis Using Network of Instruments (BAQUNIN) supersite, in the urban area of Rome (Italy). Two characteristic sea-breeze patterns were identified: the front days, in which the sea-breeze front develops in a few minutes, and the gentle breeze days, in which the onset of the front is gradual (more than 20 min). In the case of front days, Aerosol Optical Depth (AOD) increases during the onset of the breeze. The Ångström Exponent (AE), tropospheric and near surface NO2 amounts are almost constant during the day, while the aerosols volume size distribution follows a trimodal distribution. PM2.5 and PM10 concentrations decrease before the development of the sea-breeze front and then increase. Conversely, during gentle breeze days, AOD and AE do not change significantly. The tropospheric and near surface amount of NO2, PM2.5 and PM10 concentrations decrease, suggesting a significant dispersion of pollutants, while the aerosol size distribution shows a trimodal predominance.