Ground-based Monitoring Stations Research Articles

Maritime sector contributions on NO2 surface concentrations in major ports of the Mediterranean Basin

The aim of this study is to estimate the contribution of the maritime sector on the air quality of major Mediterranean ports with the Long Term Ozone Simulation-European Operational Smog (LOTOS-EUROS) chemical transport model, using two source apportionment methods: the brute-force emission scenario methodology and the labelling approach. With the brute-force method NO2 shipping concentrations are estimated by the ratio of the difference between two model runs - with original and reduced emissions - and the equivalent emission reduction factor. In the labelling, emitted species are explicitly labelled per sector and tracked through all model processes. Simulations are performed for a one-year period, from May 2018 to May 2019 and the two methodologies are intercompared. The methods show strong agreement between NO2 shipping contributions (R > 0.95) and differences of ∼14%. A sensitivity analysis carried out using the brute-force method indicates that a linear regime between NOX emissions and NO2 concentrations holds, when adopting a low to moderate emission reduction (<50%). We applied the brute-force method with an assumed 20% emission reduction and found that the NO2 surface concentrations attributed to maritime sector activities in selected ports were between 5% and 70% of the total NO2 surface concentrations, with a mean value of 27%. Comparisons between measurements from the European Environment Agency (EEA) ground-based monitoring stations and LOTOS-EUROS NO2 surface concentrations show a strong correlation (R∼0.8) with an underestimation of the model (∼-32%) for the whole period. The bias is reduced to −20% when air quality monitoring stations affected by traffic and industrial activities are excluded from the analysis. Moreover, observed Sentinel-5 Precursor TroPospheric MOnitoring Instrument (S5P/TROPOMI) and modelled NO2 vertical column densities (VCDs) show a significant spatial agreement (R∼0.86) for both summer and winter with biases of −25% and −1%, respectively, over the selected ports. These comparisons were carried out as an indirect way of validating the applied methodology and the performance of the model in coastal areas. The present study provides a solid background which will enable the assimilation of the satellite observations to the CTM to infer NOX shipping emissions in the Mediterranean Basin in view of the upcoming designation of the Mediterranean Sea as an Emission Control Area in 2025.

Evaluation and comparison of MODIS and MISR aerosol products with ground-based monitoring stations in the Amazon Basin

This study evaluates the long-term accuracy of six daily aerosol optical depth (AOD) datasets derived from the Moderate Resolution Imaging Spectroradiometers (MODIS) Dark Target (DT) at 3 and 10 km resolution, Deep Blue (DB), combined DT and DB datasets (DTB) and MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms and the Multi-angle Imaging SpectroRadiometer (MISR) Version 23, by comparing them with ground-based AOD observations in 18 Aerosol Robotic Network (AERONET) sites over the Amazon basin between 2000 and 2022. The AOD retrieval accuracy was also assessed under varying seasons, aerosol loading, particle size, elevation and land cover type. Overall results showed that Terra and Aqua-MODIS DB and MISR algorithms perform better, with about 88% of AOD retrievals within the expected error range. However, MISR presented a systematic high negative bias. MODIS DT 3 km overestimated AOD values and indicated unsatisfactory retrieval accuracy in almost all scenarios. MODIS and MISR retrievals showed improved performance under low aerosol loading, whereas Aqua-MODIS MAIAC under intermediate conditions (AOD>0.2). MISR indicated a large dependence on a coarse mode-dominated environment with considerable negative bias. Terra-MODIS MAIAC retrievals exhibited a smaller dependence for aerosol particle size distribution, while Terra-MODIS DB performed better for fine mode and Aqua-MODIS DB for coarse dominance. MAIAC also performed well under polluted scenarios in the dry season in contrast to the majority of satellite-based AOD retrievals, which showed good agreement under clean background conditions in the wet season. Almost all satellite-based retrievals showed higher uncertainty under extremely high altitudes (>3000 m) followed by non-satisfactory correlation and significant biases. Additionally, almost all satellite-based AOD retrievals indicated poor performance as vegetation coverage increases. On the contrary, MISR showed higher accuracy under forest type. In general, MODIS and MISR retrieval accuracy demonstrated distinct levels of dependencies that require further improvement in the Amazon Basin.

Exposure to particulate matter and ozone, locations of regulatory monitors, and sociodemographic disparities in the city of Rio de Janeiro: Based on local air pollution estimates generated from machine learning models

South America is underrepresented in research on air pollution exposure disparities by sociodemographic factors, although such disparities have been observed in other parts of the world. We investigated whether exposure to and information about air pollution differs by sociodemographic factors in the city of Rio de Janeiro, the second most populous city in Brazil with dense urban areas, for 2012–2017. We developed machine learning-based models to estimate daily levels of O3, PM10, and PM2.5 using high-dimensional datasets from satellite remote sensing, atmospheric and land variables, and land use information. Cross-validations demonstrated good agreement between the estimated levels and measurements from ground-based monitoring stations: overall R2 of 76.8 %, 63.9 %, and 69.1 % for O3, PM2.5, and PM10, respectively. We conducted univariate regression analyses to investigate whether long-term exposure to O3, PM2.5, PM10 and distance to regulatory monitors differs by socioeconomic indicators, the percentages of residents who were children (0-17 years) or age 65+ years in 154 neighborhoods. We also examined the number of days exceeding the Brazilian National Air Quality Standard (BNAQS). Long-term exposures to O3 and PM2.5 were higher in more socially deprived neighborhoods. An interquartile range (IQR) increment of the social development index (SDI) was associated with a 3.6 μg/m3 (95 % confidence interval [CI]: 2.9, 4.4; p-value≤0.001) decrease in O3, and 0.3 μg/m3 (95 % CI: 0.2, 0.5; p-value = 0.010) decrease in PM2.5. An IQR increase in the percentage of residents who are children was associated with a 4.1 μg/m3 (95 % CI: 3.1, 5.0; p-value≤0.001) increase in O3, and 0.4 μg/m3 (95 % CI: 0.3, 0.6; p-value = 0.009) increase in PM2.5. An IQR increase in the percentage of residents age ≥65was associated with a 3.3 μg/m3 (95 % CI: 2.4, 4.3; p-value=<0.001) decrease in O3, and 0.3 μg/m3 (95 % CI: 0.1, 0.5; p-value = 0.058) decrease in PM2.5. There were no apparent associations for PM10. The association for daily O3 levels exceeding the BNAQS daily standard was 0.4 %p–0.8 %p different by the IQR of variables, indicating a 7–15 days difference in the six-year period. The association for daily PM2.5 levels exceeding the BNAQS daily standard showed a 0.7–1.5 %p difference by the IQR, meaning a 13–27 days difference in the period. We did not find statistically significant associations between the distance to monitors and neighborhood characteristics but some indication regarding SDI. We found that O3 levels were higher in neighborhoods situated farther from monitoring stations, suggesting that elevated levels of air pollution may not be routinely measured. Exposure disparity patterns may vary by pollutants, suggesting a complex interplay between environmental and socioeconomic factors in environmental justice.

Метод определения параметров движения космических аппаратов на основе радиоинтерферометрических измерений со сверхдлинной базой

The proposed method for measuring current navigational parameters of spacecrafts makes it possible to control them to a certain extent using a single ground-based automated station. With this the following specifics of a single-station control are taken into account: location of spacecraft spatial-time monitoring stations, geometrical interpretation of these measurements, difficulties in processing of the received information and proper selection of radio equipment required for performing such measurements. The proposed method for trajectory tracking is based on the ultra-long-baseline interferometry. Measuring base here is the distance between a ground-based radio complex and onboard radio complex of the reference spacecraft that is constantly visible by the ground-based monitoring station. Onboard radio complex can include an array of spacecrafts traveling either along elongated elliptical orbit with an apogee height of more than twenty thousand kilometers or along a 36000 km high geostationary orbit. Application of the proposed method will help extend capabilities of the space monitoring system in terms of prompt clarification of public and private catalogues of spacecrafts orbiting the Earth.

Spatiotemporal Variation and Driving Factors for NO2 in Mid-Eastern China

Nitrogen dioxide (NO2) is one of the major air pollutants in cities across mid-eastern China. Comprehending the spatial and temporal dynamics of NO2 drivers in various urban areas is imperative for tailoring effective air control strategies. Using data from ground-based monitoring stations, we investigated the impact of socioeconomic and meteorological factors on NO2 concentrations in cities in mid-eastern China from 2015 to 2021 using the Geographically and Temporally Weighted Regression (GTWR) model. The findings reveal a notable reduction of over 10% in NO2 concentrations since 2015 in most cities, notably a 50.5% decrease in Bozhou. However, certain areas within Anhui and Jiangsu have experienced an increase in NO2 concentrations. Significant spatial heterogeneity is observed in the relationship between NO2 concentrations and influencing factors. The permanent population density (POP) and the electricity consumption (EC) of the entire society exhibited the strongest correlations with NO2 concentrations, with average coefficients of 0.431 and 0.520, respectively. Furthermore, other economic factors such as urbanization rate (UR), the share of secondary sector output in total GDP (IS), and the coverage rate of urban green areas (CG) were predominantly positively correlated, while GDP per capita (PGDP) and civil car vehicles (CV) demonstrated primarily negative correlations. Furthermore, we examined the correlations between four meteorological factors (temperature, relative humidity, wind speed, and precipitation) and NO2 concentrations. All these factors exhibited negative correlations with NO2 concentrations. Among them, temperature exhibited the strongest negative correlation, with a coefficient of −0.411. This research may contribute valuable insights and guidance for developing air emission reduction policies in various cities in mid-eastern China.

Assessment of COVID-19 lockdown impact on PM10 and PM2.5 concentrations in the Mexico City Metropolitan Area

PM10 and PM2.5 concentrations are regularly monitored because they cause adverse effects on human health. The concentrations of PM10 and PM2.5 vary within a city due to land use, the amount of traffic, and pollution sources. As the COVID-19 pandemic spread around the world, lockdowns were enacted to stop the spread of the virus. These lockdowns meant reduced mobility and the halt of certain economic activities which had an impact on air pollution sources. In this paper, we assess the effect of the lockdown enacted in April-May 2020 in the Mexico City Metropolitan Area on PM10 and PM2.5 concentrations, using statistical analysis tools. MODIS-derived aerosol optical depth is used to assess the change of aerosols spatial distribution during the lockdown. Furthermore, a Mann-Whitney test is applied to PM10 and PM2.5 data from ground-based monitoring stations to evaluate the effects of lockdown. A general reduction of 20 % of PM10 and 10 % in PM2.5 was found. Overall, a 15 % reduction in particulate matter was found in Mexico City due to lockdown.

Atmospheric heating in the US from saharan dust: Tracking the June 2020 event with surface and satellite observations

In June 2020, a record level of Saharan dust was transported across the Atlantic Ocean impacting air quality in the Caribbean Basin and the United States (US). Satellite images showed the transport, while a series of ground-based monitoring stations captured the surface impacts as the Saharan dust was transported to the Caribbean basin and then moved into the Southern (Houston, Texas; WL), Eastern (Bone, North Carolina; APP) and Midwestern (Bondville, Illinois; BND) US. The present study characterizes the Saharan dust event (SDE) using a comprehensive set of satellite observations and in-situ measurements of particulate matter (PM), and aerosol optical properties (AOPs; σscat: scattering coefficient, σabs: absorption coefficient, SAE: scattering Ångström exponent and AAE: absorption Ångström exponent). The Saharan dust intrusion was identified at each site by a marked increase in σscat, and AAE and a simultaneous decrease in SAE. A maximum hourly average PM2.5 concentration and σscat (525 nm) of 97.5 μg m−3 and 190 Mm−1 was observed at WL during the SDE. The maximum hourly average σscat (550 nm) for PM10 size cut of 215 and 66.9 Mm−1 was observed at APP and BND, respectively, during the SDE. The AAE at each site reached above 1.3 with an average value of 1.39 ± 0.35, 3.34 ± 0.56 and 1.65 ± 0.27 at WL, APP, and BND, respectively, during the SDE, whereas the average SAE dropped below 0.5 at WL and APP and below 0.9 at BND. The results demonstrated that the identification of SDE using AOPs depends largely on the location of the measurement, which determines the background characteristics of a site. The results suggest that other intensive AOPs like asymmetry parameter (g) and single scattering albedo Ångström exponent (SSAAE) can also be exploited to characterize SDE. Aerosol radiative forcing (ARF) estimates indicate that the SDE resulted in the heating of the atmosphere (+5.37 to +11.8 W m−2) at the rate of 0.11–0.24 K day−1 at the US sites. This atmospheric heating from transported Saharan dust can alter the regional atmospheric dynamics and is relevant to understand potential changes in regional climate.

Relationships between ozone and particles during air pollution episodes in arid continental climate

For human health, tropospheric ozone (O3), particles (PM2.5 and PM10, particles with aerodynamic diameter <2.5 and 10 μm), and nitrogen dioxide (NO2) are the most harmful air pollutants. We have investigated the simultaneity of PM2.5, PM10, NO2, and O3 occurrence by using data from 21 ground-based monitoring stations in a megacity of the Middle East, Tehran (Iran), between 2011 and 2022. In Tehran, the daily PM2.5 (21–45 μg m−3), PM10 (52–108 μg m−3) and NO2 (75–146 μg m−3) mean concentrations have largely exceeded the 24-h limit value established by the 2021 World Health Organization for the protection of human health, i.e., 15, 45 and 25 μg m−3, respectively, in particular for NO2. The ground-level daily O3 concentrations ranged from 26 to 47 μg m−3. Changes in aerosols burden (e.g., PM2.5 and PM10) affect surface O3 through changes in aerosol chemistry and photolysis rates. Following the dust events and for the days with PM2.5 and PM10 exceeding 50 and 100 μg m−3, respectively, we observed a reduction of surface O3 levels (- 30.0% and - 13.5%), concurrently to surface NO2 (+27.9% and +16.6%) levels increases compared to the non-dusty clear-sky days over Tehran city. Surface O3 formation is suppressed under high particulate matter (PM) levels in the atmosphere, in particular PM2.5, likely due to weakened photochemical reactions (lower solar radiation and air temperature), dilution effect, and heterogeneous chemical processes occurring onto the PM2.5 surface (reactive uptake of nitrogen oxides, NOx, and hydrogen oxide radicals, HOx). To achieve O3-PM co-improvement in cities, we recommend reductions in NOx emissions concurrently with significant reductions in PM and Volatile Organic Compounds emissions, for instance by suitable greening and re-naturing programs.

Status of atmospheric air pollution in Ukraine prior to the full-scale russian invasion. Part 1: ground-level content of pollutants

The paper is dedicated to examination of atmospheric air pollution in Ukraine that is aimed at establishing basic air quality conditions prior to the full-scale Russian invasion. Analyses cover five air pollutants (dust, sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), and formaldehyde (CH2O)) across 126 monitoring stations in 39 cities. It was found that the most dangerous excess of air pollution levels in cities was associated with dust, NO2, and CO. At the same time, SO2 content usually did not reach dangerous levels. The highest frequency of exceeding the threshold one-time levels was common for the cities with well-developed industrial facilities. Dangerous air pollution was observed in 15–60% of cases, depending on a pollutant. Dust and NO2 concentrations of the most polluted cities were 10 times higher than their concentrations in smaller cities having no significant industrial emissions. The difference in average CO, SO2, and CH2O concentrations among monitoring stations constituted 3-5 times. We studied the patterns of pollutants' seasonal variability recorded at the monitoring stations. Normally, no significant seasonality except for CH2O and dust was observed. The interannual variability of pollutants, emissions, and their trends were analyzed for the period since 2008. We identified a certain discrepancy in trends between pollutants’ concentrations and emission data over the last years. Pollutant content often continued to grow concurrently with emission reductions. The impact of atmospheric phenomena and wind parameters was studied mainly in terms of its role in formation of high and low atmospheric pollution levels. The analysis indicated a crucial role of local conditions in the formation of an atmospheric pollution field next to monitoring stations. Varying atmospheric pollution values across different monitoring stations within a specific city can be different even under the influence of the same atmospheric phenomena. The wind impact also formed different patterns of atmospheric pollution within same city. 12% to 22% of all monitoring stations (depending on a pollutant) indicated a prevailing impact of a single emission source (or a combined impact of a group of sources). At the same time, air pollution at other monitoring stations was highly variable, with elevated pollution levels being transported from numerous directions. The analysis of the air pollution-affected condition presented in the paper could be used as a basis for comparing ground-level pollution and assessing the warfare consequences in Ukraine. The research emphasizes the importance of updating the information about ground-based monitoring stations.

Deformation Monitoring and Analysis of Reservoir Dams Based on SBAS-InSAR Technology—Banqiao Reservoir

Most dams in China have been operating for a long time and are products of the economic and technical limitations at the time of construction. Due to decades of aging engineering and ancillary problems, these reservoirs pose great threats to the safety of local people and the development of the surrounding economy. In this study, the surface deformation information for the Banqiao Reservoir is monitored with the small baseline subset–synthetic aperture radar interferometry (SBAS-InSAR) method using 80 Sentinel-1A images acquired from 3 January 2020 to 20 August 2022. Additionally, ground measurements from the BeiDou ground-based deformation monitoring stations were collected to validate the InSAR results. Based on the InSAR results, the spatiotemporal deformation features of the dam were analyzed in detail. The results show that the deformation in most areas, including the dam in the study area, is relatively stable, and the regional deformation velocity of the Banqiao Reservoir dam and other hydraulic engineering facilities varies between −1 mm/y and −4 mm/y. The Ru River area has a relatively obvious subsidence trend, and the maximum subsidence velocity reaches 30 mm/y. The InSAR monitoring results are consistent with the change trend in the BeiDou ground-based deformation measurement results. The monitoring results for the reservoir dam area provide a reference for local sustainable development and geological disaster prevention.

Selection of statistical technique for imputation of single site-univariate and multisite-multivariate methods for particulate pollutants time series data with long gaps and high missing percentage.

Monitoring air contaminants has become essential to exposure science, toxicology, and public health research. However, missing values are common while monitoring air contaminants, especially in resource-constrained settings such as power cuts, calibration, and sensor failure. In contaminants monitoring, evaluating existing imputation techniques for dealing with recurrent periods of missing and unobserved data are limited. The proposed study aims to perform a statistical evaluation of six univariate and four multivariate time series imputation methods. The univariate methods are based on inter-time correlation characteristics, and the multivariate approach considers muti-site to impute missing data. The present study retrieved data from 38 ground-based monitoring stations for particulate pollutants in Delhi for 4 years. For univariate methods, missing values were simulated under 0-20% (5%, 10%, 15%, and 20%), and high 40%, 60%, and 80% missing levels having long gaps. Before evaluating multivariate methods, input data underwent pre-processing steps: selecting the target station to be imputed, choosing covariates based on the spatial correlation between multiple sites, and framing a combination of target and neighbouring stations (covariates) under 20%, 40%, 60%, and 80%. Next, the particulate pollutants data of 1480 days is provided as input to four multivariate techniques. Finally, the performance of each algorithm was evaluated using error metrics. The results show that the long interval time series data and spatial correlation of multiple stations significantly improved outcomes for univariate and multivariate time series methods. The univariate Kalman_arima performs well for long-missing gaps and all missing levels (except for 60-80%), yielding low error and high R2 and d values. In contrast, multivariate MIPCA performed better than Kalman-arima for all target stations with the highest missing percentage.

Estimation of PM2.5 concentrations with high spatiotemporal resolution in Beijing using the ERA5 dataset and machine learning models

PM2.5 is the main component of most haze, and the presence of high concentrations of PM2.5 in the air for an extended time can cause serious effects on human health, so there is an urgent need for research work related to PM2.5. Traditional PM2.5 monitoring uses ground-based monitoring stations with low spatial resolution. Other studies have retrieved the Moderate Resolution Imaging Spectroradiometer aerosol optical depth product by the dark-target algorithm. However, the estimated PM2.5 concentration on the ground will produce missing values, which will lead to the reduction of spatial and temporal resolution. Based on this, this study proposes a machine learning algorithm to estimate PM2.5 by using the fifth generation reanalysis (ERA5) data set published by the European Center for Medium-range Weather Forecasts (ECMWF). In this study, two different methods of back propagation neural network (BPNN) and random forest (RF) were used to develop the models. Firstly, the meteorological parameters (precipitable water vapor, water vapor pressure and relative humidity, etc.) and pollution parameters (O3, CO, NO2, SO2, PM10, and PM2.5) were used to establish PM2.5 model in 2021. The results showed that the R2 and RMSE for BPNN and RF were 0.94/0.96 and 10.37/8.77 µg/m3, respectively. Then, due to the lack and the low spatial resolution of the pollution parameters, using only the ERA5 meteorological data with the high spatiotemporal resolution to develop the PM2.5 model in winter, the R2 of the RF model (0.93) was 0.05 higher and the RMSE (12.50 µg/m3) was 4.19 µg/m3 lower than that of the BPNN model, which indicates that it is feasible to develop the PM2.5 model using only meteorological parameters. Finally, using the RF model of the second stage and ERA5 meteorological data with a spatial resolution of 0.05° (obtained by cubic spline interpolation) to generate the hourly PM2.5 map of Beijing and compare it with China High Air Pollutants dataset, the R2 and RMSE of Beijing were 0.78 µg/m3 and 14.85 µg/m3, respectively. On this basis, it is found that the areas with high PM2.5 concentration are close to the areas with serious pollution in Hebei Province by analyzing the PM2.5 map of Beijing, and area transport and human activities are important sources of air pollution.

Towards monitoring the CO2 source–sink distribution over India via inverse modelling: quantifying the fine-scale spatiotemporal variability in the atmospheric CO2 mole fraction

Abstract. Improving the estimates of CO2 sources and sinks over India through inverse methods calls for a comprehensive atmospheric monitoring system involving atmospheric transport models that make a realistic accounting of atmospheric CO2 variability along with a good coverage of ground-based monitoring stations. This study investigates the importance of representing fine-scale variability in atmospheric CO2 in models for the optimal use of observations through inverse modelling. The unresolved variability in atmospheric CO2 in coarse models is quantified by using WRF-Chem (Weather Research and Forecasting model coupled with Chemistry) simulations at a spatial resolution of 10 km × 10 km. We show that the representation errors due to unresolved variability in the coarse model with a horizontal resolution of 1∘ (∼ 100 km) are considerable (median values of 1.5 and 0.4 ppm, parts per million, for the surface and column CO2, respectively) compared to the measurement errors. The monthly averaged surface representation error reaches up to ∼ 5 ppm, which is even comparable to half of the magnitude of the seasonal variability or concentration enhancement due to hotspot emissions. Representation error shows a strong dependence on multiple factors such as time of the day, season, terrain heterogeneity, and changes in meteorology and surface fluxes. By employing a first-order inverse modelling scheme using pseudo-observations from nine tall-tower sites over India, we show that the net ecosystem exchange (NEE) flux uncertainty solely due to unresolved variability is in the range of 3.1 % to 10.3 % of the total NEE of the region. By estimating the representation error and its impact on flux estimations during different seasons, we emphasize the need to take account of fine-scale CO2 variability in models over the Indian subcontinent to better understand processes regulating CO2 sources and sinks. The efficacy of a simple parameterization scheme is further demonstrated to capture these unresolved variations in coarse models.

Performance of dual one-way measurements and precise orbit determination for BDS via inter-satellite link

Abstract The continuous full operation of the constellation of BeiDou navigation satellite system (BDS) provides favorable conditions for the performance evaluation of the BDS inter-satellite links (ISLs). The primary features of ISLs that affect the precision of precise orbit determination (POD) and time synchronization include (i) the spatiotemporal coverage or continuity of observations; (ii) the observational accuracy, such as observation noise and bias; and (iii) the observational geometry represented by dilution of precision. After comparing some technical features and the current status of the Global Navigation Satellite Systems ISLs, the measurement principle of dual one-way ISLs for BDS and its data processing methods are presented. The performance evaluation involving the above three aspects was carried out using 14 days of ISL data, with some typical indicators derived. POD based on data fusion of ISLs and ground-based L-band monitoring stations was conducted, with root-mean-square of posterior residuals of about 5.0 cm, and MEO radial accuracy better than 1.0 cm. The results show that ISLs offer crucial support for BDS to provide global high-precision services under regional monitoring network conditions.

Estimating Full-Coverage PM2.5 Concentrations Based on Himawari-8 and NAQPMS Data over Sichuan-Chongqing

Fine particulate matter (PM2.5) has attracted extensive attention due to its harmful effects on humans and the environment. The sparse ground-based air monitoring stations limit their application for scientific research, while aerosol optical depth (AOD) by remote sensing satellite technology retrieval can reflect air quality on a large scale and thus compensate for the shortcomings of ground-based measurements. In this study, the elaborate vertical-humidity method was used to estimate PM2.5 with the spatial resolution 1 km and the temporal resolution 1 hour. For vertical correction, the scale height of aerosols (Ha) was introduced based on the relationship between the visibility data and extinction coefficient of meteorological observations to correct the AOD of the Advance Himawari Imager (AHI) onboard the Himawari-8 satellite. The hygroscopic growth factor (f(RH)) was fitted site-by-site and month by month (1–12 months). Meanwhile, the spatial distribution of the fitted coefficients can be obtained by interpolation assuming that the aerosol properties vary smoothly on a regional scale. The inverse distance weighted (IDW) method was performed to construct the hygroscopic correction factor grid for humidity correction so as to estimate the PM2.5 concentrations in Sichuan and Chongqing from 09:00 to 16:00 in 2017–2018. The results indicate that the correlation between “dry” extinction coefficient and PM2.5 is slightly improved compared to the correlation between AOD and PM2.5, with r coefficient values increasing from 0.12–0.45 to 0.32–0.69. The r of hour-by-hour verification is between 0.69 and 0.85, and the accuracy of the afternoon is higher than that of the morning. Due to the missing rate of AOD in the southwest is very high, this study utilized inverse variance weighting (IVW) gap-filling method combine satellite estimation PM2.5 and the nested air-quality prediction modeling system (NAQPMS) simulation data to obtain the full-coverage hourly PM2.5 concentration and analyze a pollution process in the fall and winter.

Air quality observations onboard commercial and targeted Zeppelin flights in Germany – a platform for high-resolution trace-gas and aerosol measurements within the planetary boundary layer

Abstract. A Zeppelin airship was used as a platform for in situ measurements of greenhouse gases and short-lived air pollutants within the planetary boundary layer (PBL) in Germany. A novel quantum cascade laser-based multi-compound gas analyzer (MIRO Analytical AG) was deployed to simultaneously measure in situ concentrations of greenhouse gases (CO2, N2O, H2O, and CH4) and air pollutants (CO, NO, NO2, O3, SO2, and NH3) with high precision at a measurement rate of 1 Hz. These measurements were complemented by electrochemical sensors for NO, NO2, Ox (NO2 + O3), and CO, an optical particle counter, temperature, humidity, altitude, and position monitoring. Instruments were operated remotely without the need for on-site interactions. Three 2-week campaigns were conducted in 2020 comprising commercial passenger as well as targeted flights over multiple German cities including Cologne, Mönchengladbach, Düsseldorf, Aachen, Frankfurt, but also over industrial areas and highways. Vertical profiles of trace gases were obtained during the airship landing and take-off. Diurnal variability of the Zeppelin vertical profiles was compared to measurements from ground-based monitoring stations with a focus on nitrogen oxides and ozone. We find that their variability can be explained by the increasing nocturnal boundary layer height from early morning towards midday, an increase in emissions during rush hour traffic, and the rapid photochemical activity midday. Higher altitude (250–450 m) NOx to CO ratios are further compared to the 2015 EDGAR emission inventory to find that pollutant concentrations are influenced by transportation and residential emissions as well as manufacturing industries and construction activity. Finally, we report NOx and CO concentrations from one plume transect originating from a coal power plant and compare it to the EURopean Air pollution Dispersion-Inverse Modell (EURAD-IM) model to find agreement within 15 %. However, due to the increased contribution of solar and wind energy and the impact of lockdown measures the power plant was operating at max. 50 % capacity; therefore, possible overestimation of emissions by the model cannot be excluded.

Particulate air pollution in Durban: Characteristics and its relationship with 1 km resolution satellite aerosol optical depth

Investigations on the characteristics of air pollution in African regions are fewer compared to other parts of the world. This study presents the temporal characteristics of PM2.5 and PM10 (mass concentration of particulate matter with aerodynamic size less than or equal to 2.5 and 10 µm, respectively) and their ratio (PM2.5/PM10) over Durban, South Africa for the period 2019–2020. The study further explores the satellite aerosol optical depth (AOD) and particulate matter (PM) relationship using an advanced statistical model. The PM data used in this study is obtained from ground-based monitoring stations being maintained by the South African Air Quality Informative System (SAAQIS) network. Within Durban, PM2.5 and PM10 showed an increasing trend toward the south. The annual mean PM2.5 (PM10) ranged between 15 and 24 μg m−3 (37 and 54 μg m−3) across the four study sites in Durban. Winter is observed to be the most polluted season among all, with daily mean PM levels exceeding the World Health Organization (WHO) standards. PM2.5, PM10, and the PM2.5/PM10 ratios exhibited pronounced diurnal variations. Concentration Weighted Trajectory (CWT) analysis identified the potential non-local source regions and long-range transportation pathways of PM. Investigation into the impact of COVID-19 lockdown on PM revealed a maximum of 15% reduction compared to a business-as-usual (BAU) year. The observed reduction in PM during lockdown is compared and contrasted with that observed in other cities worldwide. The statistical model’s tenfold cross-validation (CV) coefficient of determination (R2) ranged between 0.6 and 0.8. The model predictions can be highly useful in the spatial mapping of PM over the measurements-deficit African regions.

Air quality in the New Delhi metropolis under COVID-19 lockdown

Air pollution has been on continuous rise with increase in industrialization in metropolitan cities of the world. Several measures including strict climate laws and reduction in the number of vehicles were implemented by several nations. The COVID-19 pandemic provided a great opportunity to understand the daily human activities effect on air pollution. Majority nations restricted industrial activities and vehicular traffic to a large extent as a measure to restrict COVID-19 spread. In this paper, we analyzed the impact of such COVID19-induced lockdown on the air quality of the city of New Delhi, India. We analyzed the average concentration of common gaseous pollutants viz. sulfur dioxide (SO), ozone (O), nitrogen dioxide (NO), and carbon monoxide (CO). These concentrations were obtained from the tropospheric column of Sentinel-5P (an earth observation satellite of European Space Agency) data. We observed that the city observed a significant drop in the level of atmospheric pollutant’s concentration for all the major pollutants as a result of strict lockdown measure. Such findings are also validated with pollutant data obtained from ground based monitoring stations. We observed that near-surface pollutant concentration dropped significantly by 50% for PM2.5, 71.9% for NO, and 88% for CO, after the lockdown period. Such studies would pave the path for implementing future air pollution control measures by environmentalists.

Aridification in a farming-pastoral ecotone of northern China from 2 perspectives: Climate and soil

Understanding the impact of climate change on terrestrial wet and dry changes and the relationship between the two is of great significance to the sustainable development of terrestrial ecosystems. The farming-pastoral ecotone of northern China (FPENC) is an area that is sensitive to climate change, suffering from perennial drought and a clear aridification trend. Unlike previous single-factor, single-timescale studies, we identified aridification in the region based on a dataset established by remote sensing and ground-based monitoring stations from a combination of two perspectives: climate and soil. The results show that, in terms of climate, the period from 2000 to 2019 was the driest in the region during the last 120 years , and the summer drought was the most severe and shifted from a summer to spring drought; in terms of soil, the soil aridification trend in the region was severe, with 16.1% of the areas becoming significantly drier (P < 0.1) among the years and 41.6% in spring, respectively. Similar to climate change, soils exhibited recessive aridification due to the counterbalancing effect of the dry and wet seasons within the year. Then the coupling relationship between climate change and soil aridification was established in time and space. Moreover, the spatiotemporal response patterns of both were obtained. The results showed that the frequency of soil drought under meteorological drought conditions showed an increasing trend and that the sensitivity of soil drought occurrence increased. Among them, the effect of precipitation on relative soil moisture (RSM) was immediate, and the effect of prolonged warming on RSM is greater. The area of soil aridification that was caused by climatic aridification in spring accounted for 13.7% of the entire area. The regional aridification research mode proposed in this paper can provide ideas for subsequent studies.

Analysis of atmospheric ammonia over South and East Asia based on the MOZART-4 model and its comparison with satellite and surface observations

Abstract. Limited availability of atmospheric ammonia (NH3) observations limits our understanding of controls on its spatial and temporal variability and its interactions with the ecosystem. Here we used the Model for Ozone and Related chemical Tracers version 4 (MOZART-4) global chemistry transport model and the Hemispheric Transport of Air Pollution version 2 (HTAP-v2) emission inventory to simulate global NH3 distribution for the year 2010. We presented a first comparison of the model with monthly averaged satellite distributions and limited ground-based observations available across South Asia. The MOZART-4 simulations over South Asia and East Asia were evaluated with the NH3 retrievals obtained from the Infrared Atmospheric Sounding Interferometer (IASI) satellite and 69 ground-based monitoring stations for air quality across South Asia and 32 ground-based monitoring stations from the Nationwide Nitrogen Deposition Monitoring Network (NNDMN) of China. We identified the northern region of India (Indo-Gangetic Plain, IGP) as a hotspot for NH3 in Asia, both using the model and satellite observations. In general, a close agreement was found between yearly averaged NH3 total columns simulated by the model and IASI satellite measurements over the IGP, South Asia (r=0.81), and the North China Plain (NCP), East Asia (r=0.90). However, the MOZART-4-simulated NH3 column was substantially higher over South Asia than East Asia, as compared with the IASI retrievals, which show smaller differences. Model-simulated surface NH3 concentrations indicated smaller concentrations in all seasons than surface NH3 measured by the ground-based observations over South and East Asia, although uncertainties remain in the available surface NH3 measurements. Overall, the comparison of East Asia and South Asia using both MOZART-4 model and satellite observations showed smaller NH3 columns in East Asia compared with South Asia for comparable emissions, indicating rapid dissipation of NH3 due to secondary aerosol formation, which can be explained by larger emissions of acidic precursor gases in East Asia.