Ground-based Monitoring Stations Research Articles

The Impacts of the COVID-19 Lockdown on Air Quality in the Guanzhong Basin, China

The Corona Virus Disease 2019 (COVID-19) appeared in Wuhan, China, at the end of 2019, spreading from there across China and within weeks across the whole world. In order to control the rapid spread of the virus, the Chinese government implemented a national lockdown policy. It restricted human mobility and non-essential economic activities, which, as a side effect, resulted in the reduction of the emission of pollutants and thus the improvement of the air quality in many cities in China. In this paper, we report on a study on the changes in air quality in the Guanzhong Basin during the COVID-19 lockdown period. We compared the concentrations of PM2.5, PM10, SO2, NO2, CO and O3 obtained from ground-based monitoring stations before and after the COVID-19 outbreak. The analysis confirmed that the air quality in the Guanzhong Basin was significantly improved after the COVID-19 outbreak. During the emergency response period with the strictest restrictions (Level-1), the concentrations of PM2.5, PM10, SO2, NO2 and CO were lower by 37%, 30%, 29%, 52% and 33%, respectively, compared with those before the COVID-19 outbreak. In contrast, O3 concentrations increased substantially. The changes in the pollutant concentrations varied between cities during the period of the COVID-19 pandemic. The highest O3 concentration changes were observed in Xi’an, Weinan and Xianyang city; the SO2 concentration decreased substantially in Tongchuan city; the air quality had improved the most in Baoji City. Next, to complement the sparsely distributed air quality ground-based monitoring stations, the geographic and temporally weighted regression (GTWR) model, combined with satellite observations of the aerosol optical depth (AOD) and meteorological factors was used to estimate the spatial and temporal distributions of PM2.5 and PM10 concentrations with a resolution of 6 km × 6 km before and after the COVID-19 outbreak. The model was validated by a comparison with ground-based observations from the air quality monitoring network in five cities in the Guanzhong Basin with excellent statistical metrics. For PM2.5 and PM10 the correlation coefficients R2 were 0.86 and 0.80, the root mean squared errors (RMSE) were 11.03 µg/m3 and 14.87 µg/m3 and the biases were 0.19 µg/m3 and −0.27 µg/m3, which led to the conclusion that the GTWR model could be used to estimate the PM concentrations in locations where monitoring data were not available. Overall, the PM concentrations in the Guanzhong Basin decreased substantially during the lockdown period, with a strong initial decrease and a slower one thereafter, although the spatial distributions remained similar.

Remote sensing of two exceptional winter aerosol pollution events and representativeness of ground-based measurements

Abstract. Two intense winter aerosol pollution events, which took place in winter 2016–2017 in Paris, were monitored using a ground-based N2-Raman lidar, in the framework of WASLIP (Winter Aerosol Survey by Lidar In Paris), a dedicated field campaign that was carried out in this area from 1 November 2016 to 31 January 2017. The data analysis uses the synergy between ground-based and spaceborne lidar observations and data from the air quality monitoring network Airparif. The first severe aerosol pollution event began on 30 November 2016 and ended on 2 December, concerning a circular area of ∼250 km in diameter around Paris. The maximum PM10 was 121±63 µg m−3 (regional spatial average ± SD) for the Airparif ground-based PM monitoring stations, and the aerosol extinction coefficient (AEC) ranged from 0.2 to 1 km−1. The second event took place from 20 to 23 January which covered all of the northwestern Europe, with maxima of PM10 around 156±33 µg m−3 and AEC between 0.6 and 1 km−1, within the winter atmospheric boundary layer. Although these two major aerosol pollution events did not occur under identical anticyclonic weather conditions, they share very low planetary boundary layer (PBL) heights, down to 300 m above ground level. Moreover, they are associated with significantly different aerosol lidar ratios: 72±15 and 56±15 sr, respectively in December and January. Such results are consistent with available spaceborne lidar data, 70±25 sr from CALIOP (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), and values found in the literature. During these two events, the continuous temporal evolution of the aerosol extinction coefficient allows us to investigate the representativeness of optical parameters found in the planetary boundary layer to assess surface aerosol concentration. No one-to-one relationship between the aerosol optical thickness (AOT) and PM2.5 values stands out within our study. In contrast, the maximum aerosol extinction coefficient found within the planetary boundary layer correlates well with PM2.5 at the ground (R2∼0.75, specific extinction cross section of 9.4 m2 g−1) for these polluted events. Thus this lidar-derived aerosol extinction coefficient is identified as a consistent variable to monitor the pollution during winter events.

Improved representation of particle size and solubility in model simulations of atmospheric dispersion and wet-deposition from Fukushima

Radionuclides released into the atmosphere following the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) accident were detected by ground-based monitoring stations worldwide. The inter-continental dispersion of radionuclides provides a unique opportunity to evaluate the ability of atmospheric dispersion models to represent the processes controlling their transport and deposition in the atmosphere. Co-located measurements of radioxenon (133Xe) and caesium (137Cs) concentrations enable individual physical processes (dispersion, dry and wet deposition) to be isolated. In this paper we focus on errors in the prediction of 137Cs attributed to the representation of particle size and solubility, in the process of modelling wet deposition. Simulations of 133Xe and 137Cs concentrations using the UK Met Office NAME (Numerical Atmospheric-dispersion Modelling Environment) model are compared with CTBTO (Comprehensive Nuclear-Test-Ban Treaty Organisation) surface station measurements. NAME predictions of 137Cs using a bulk wet deposition parameterisation (which does not account for particle size dependent scavenging or solubility) significantly underestimate observed 137Cs. When a binned wet deposition parameterisation is implemented (which accounts for particle size dependent scavenging) the correlations between modelled and observed air concentrations improve at all 9 of the Northern Hemisphere sites studied and the respective RMSLE (root-mean-square-log-error) decreases by a factor of 7 due to a decrease in the wet-deposition of Aitken and Accumulation mode particles. Finally, NAME simulations were performed in which insoluble submicron particles are represented. Representing insoluble particles in the NAME simulations improves the RMSLE at all sites further by a factor of 7. Thus NAME is able to predict 137Cs with good accuracy (within a factor of 10 of observed 137Cs values) at distances greater than 10,000 km from FDNPP only if insoluble submicron particles are considered in the description of the source. This result provides further evidence of the presence of insoluble Cs-rich microparticles in the release following the accident at FDNPP and suggests that these small particles travelled across the Pacific Ocean to the US and further across the North Atlantic Ocean towards Europe.

Concentrations and Allocation of NO<sub>2</sub> Emissions to Different Sources in a Distinctive Italian Region after the COVID-19 Lockdown

Nitrogen dioxide concentrations, being short-lived pollutants, are good indicators of changes in emission sources and economic slowdowns. This analysis focuses on the Greater Salento region (Italy) and aims to monitor, by investigating the relative sources, the changes of NO2 tropospheric concentrations notoriously related to vehicular traffic exhausts and, in general, to fossil fuel combustion processes which are now apparently linked to many COVID-19 patients deaths. The principle objective of this paper is to map the tropospheric NO2 local distribution and to extrapolate, from the overall data of average daily concentrations of NO2 as recorded by the ARPA-Puglia ground-based monitoring stations, the single contributions and their mutual relationships of the different diffuse emission sources (motor vehicles and domestic heating systems) by identifying, the environmental background threshold of this pollutant of each geographic area, thanks to the simplified situation determined by the COVID-19 lockdown. The analyzed territory (the so-called “Greater Salento” or Salento Peninsula) is very unusual because there are two provinces with large industrial settlements, Taranto, with the steel area of ex-ILVA, and Brindisi, with petrochemical and thermoelectric power plants, which enclose a territory, the province of Lecce, free of any industrial plants of such sizes and their environmental impacts. From the results of this study, in addition to confirming the obvious and overall decrease of NO2 concentrations (-23.2% compared to previous year) during the lockdown period, interesting and distinctive local allocations of nitrogen dioxide concentrations to different sources have also emerged: heating household systems, and not road traffic, are the main sources of this dangerous pollutant in this region, with an average quota of 44.3%. The studied regional situation is so significant as to allow broader considerations regarding to other similar international areas.

Towards air quality modeling in Agadir City (Morocco)

The degraded outdoor air quality in Morocco has become for decision makers a topic of major concern. However, taking into account the purchase and maintenance costs for monitoring network, air quality modeling is a primordial tool to predict the air quality evolution scenarios all over the Moroccan territory. Accordingly, a first air quality modeling based on CHIMERE/WRF was for the first time implemented and tested within Agadir-City. The present study allowed obtaining preliminary spatial distribution mapping of 5 relevant pollutants concentration all over the city. Furthermore, model evaluations were compared to available data from ground-based monitoring station installed within Agadir City.

Using unmanned aerial vehicle to investigate the vertical distribution of fine particulate matter

The vertical distribution of fine particulate matter (PM2.5) is a vital link in understanding the transport and evolution of haze. However, existing ground stations cannot provide sufficient vertical observations of PM2.5, especially at fine scales regarding space and time. This study deployed a six-rotor unmanned aerial vehicle (UAV) equipped with portable monitors to observe the vertical distributions of PM2.5 and meteorological parameters within 1000 m lower troposphere. By comparing with ground-based monitoring station and tethered balloon platform for PM2.5 measurements, the UAV was improved and then used to perform a field observation experiment in the Qingpu district of Shanghai, China. The UAV-based observations showed a decreasing vertical profile of PM2.5 in the experimental day, with a decrease of more than 50% at 0–1000 m height. PM2.5 had a vertical pattern that declined rapidly after 700 m in the afternoon, but the morning PM2.5 had a rapid decline from 200 to 500 m compared with other height intervals in this period. A temperature inversion at a lower height in the morning blocked newly formed PM2.5 at ground to disperse upward, and PM2.5 above the temperature inversion was composed of residuals in last night. The temperature inversion gradually climbed up in the afternoon, which was beneficial to the dispersion of near-ground PM2.5. The difference of relative humidity above and below 700 m height implies different geographical origins that were well identified and explained by a cluster analysis. This study generally highlights the significance of using a lightweight UAV to understand air pollution and governance environments in the urban area.

Spatiotemporal analysis of dust patterns over Mesopotamia and their impact on Khuzestan province, Iran

Dust storms are yet quite frequent in various parts of the world, particularly ancient Mesopotamia (approximately corresponding to most parts of Iraq as well as certain surrounding regions toward its north). To add to the ongoing difficulty, monitoring dust patterns has been proven to be a rather difficult endeavor given the absence of reliable ground-based monitoring stations in the corresponding area. Additionally, western provinces of Iran, especially Khuzestan in the southwest of Iran, have been severely affected by dust storms carried by the westerly winds, blown through neighboring countries in ancient Mesopotamia. This study proceeds to employ aerosol optical depth (AOD), extracted from the MODerate-resolution Imaging Spectroradiometer onboard the Terra spacecraft, to assess spatial dust variations between 2001 and 2017 over the Khuzestan province and ancient Mesopotamia. The variations were also correlated with the temporal dust changes in the Khuzestan province. Frequency of occurrence for AOD > 1 was used to identify and categorize major dust sources in the aforementioned regions. The findings were indicative of an increasing trend in the annual AODs of the Khuzestan province, which eventually led to a significant increase from 2008 toward the end of 2012, but decreased again in the following years. Correspondingly, the entire time period (2001–2017) was further divided into three sub-periods: the first time period spanning from 2001 to 2007, followed by the second from 2008 to 2012 and finally a third time period from 2013 to 2017. Dust source identification was speculative of numerous dust spots in Iraq, Syria, Kuwait and also the southern parts of Khuzestan province which have become more active in recent years. Additionally, a large active dust spot was pinpointed between the northwest Iraq and eastern Syria border which has become extremely active during the second time period, possibly due to a severe drought in the Fertile Crescent.

Aerosol optical depth climatology over Central Asian countries based on Aqua-MODIS Collection 6.1 data: Aerosol variations and sources

The aim of this study is to explore the aerosol optical depth (AOD) – an indicator of air quality, based on satellite data over Central Asia. Due to limited studies and lack of knowledge regarding AOD variations over this region, five countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan) and fifteen sites including the capital cities were selected for the analysis. Long-term Aqua-MODIS Collection 6.1 Deep Blue (DB) AOD observations at 10 km spatial resolution were obtained for 2002–2017 and the Ångström exponent (AE) was calculated using spectral AOD observations. AOD and AE were explored based on seasonal and inter-annual scale. The relationship between aerosol loading and economic indicator (gross domestic product) was also analyzed. Highest frequencies of low AOD and high AE were observed in all countries. Average AOD (AE) values during the study period were 0.17 ± 0.09 (1.29 ± 0.17) for Kazakhstan, 0.11 ± 0.13 (1.41 ± 0.13) for Kyrgyzstan, 0.17 ± 0.14 (1.36 ± 0.18) for Tajikistan, 0.20 ± 0.10 (1.10 ± 0.28) for Turkmenistan, and 0.18 ± 0.09 (1.18 ± 0.24) for Uzbekistan. Summer season experienced highest aerosol loading over Kazakhstan, Tajikistan, and Uzbekistan, whereas spring season experienced highest aerosol loading over Kyrgyzstan and Turkmenistan possibly due to the influence of less precipitation and dust from the arid regions. All fifteen cities selected in this study exhibited increasing aerosol load. Aerosol types based on the relationship between AOD and AE were investigated, results suggested the clean continental aerosol as a major aerosol type over the region, whereas the contribution of mixed aerosols was observed during spring and summer seasons. This study suggests for establishment of ground-based aerosol monitoring stations network and inclusions of modeling works to understand the complex nature of aerosol over this vast landmass.

Extending the coverage area of regional ionosphere maps using a support vector machine algorithm

Abstract. The coverage of regional ionosphere maps is determined by the distribution of ground-based monitoring stations, e.g., GNSS receivers. Since ionospheric delay has a high spatial correlation, ionosphere map coverage can be extended using spatial extrapolation methods. This paper proposes a support vector machine (SVM) to extrapolate the ionosphere map data with solar and geomagnetic parameters. One year of IGS ionospheric delay map data over South Korea is used to train the SVM algorithm. Subsequently, 1 month of ionospheric delay data outside the input data region is estimated. In addition to solar and geomagnetic environmental parameters, the ionospheric delay data from the inner data region are used to estimate the ionospheric delay data for the outside region. The accuracy evaluation is performed at three levels of range −5, 10, and 15∘ outside the inner data regions. The extrapolation errors are 0.33 TECU (total electron content unit) for the 5∘ region and 1.95 TECU for the 15∘ region. These values are substantially lower than the GPS Klobuchar model error values. Comparison with another machine learning extrapolation method, the neural network, shows a substantial improvement of up to 26.7 %.

Substantial changes in air pollution across China during 2015–2017

China’s rapid industrialisation and urbanisation has led to poor air quality. The Chinese government have responded by introducing policies to reduce emissions and setting ambitious targets for ambient PM2.5, SO2, NO2 and O3 concentrations. Previous satellite and modelling studies indicate that concentrations of these pollutants have begun to decline within the last decade. However, prior to 2012, air quality data from ground-based monitoring stations were difficult to obtain, limited to a few locations in major cities, and often unreliable. Since then, a comprehensive monitoring network, with over 1000 stations across China has been established by the Ministry of Ecology and Environment (MEE). We use a three-year (2015–2017) dataset consisting of hourly PM2.5, O3, NO2 and SO2 concentrations obtained from the MEE, combined with similar data from Taiwan and Hong Kong. We find that at 53% and 59% of stations, PM2.5 and SO2 concentrations have decreased significantly, with median rates across all stations of −3.4 and −1.9 μg m−3 year−1 respectively. At 50% of stations, O3 maximum daily 8 h mean (MDA8) concentrations have increased significantly, with median rates across all stations of 4.6 μg m−3 year−1. It will be important to understand the relative contribution of changing anthropogenic emissions and meteorology to the changes in air pollution reported here.

Analysis of the Regional Ionosphere at Low Latitudes in Support of the Biomass ESA Mission

Biomass is a spaceborn polarimetric P-band (435 MHz) synthetic aperture radar (SAR) in a dawn–dusk low Earth orbit. Its principal objective is to measure biomass content and change in all the Earth’s forests. The ionosphere introduces the Faraday rotation on every pulse emitted by low-frequency SAR and scintillations when the pulse traverses a region of plasma irregularities, consequently impacting the quality of the imaging. Some of these effects are due to total electron content (TEC) and its gradients along the propagation path. Therefore, an accurate assessment of the ionospheric morphology and dynamics is necessary to properly understand the impact on image quality, especially in the equatorial and tropical regions. To this scope, we have conducted an in-depth investigation of the significant noise budget introduced by the two crests of the equatorial ionospheric anomaly (EIA) over Brazil and Southeast Asia. This paper is characterized by a novel approach to conceive a SAR-oriented ionospheric assessment, aimed at detecting and identifying spatial and temporal TEC gradients, including scintillation effects and traveling ionospheric disturbances, by means of Global Navigation Satellite Systems ground-based monitoring stations. The novelty of this approach resides in the customization of the information about the impact of the ionosphere on SAR imaging as derived by local dense networks of ground instruments operating during the passes of Biomass spacecraft. The results identify the EIA crests as the regions hosting the bulk of irregularities potentially causing degradation on SAR imaging. Interesting insights about the local characteristics of low-latitudes ionosphere are also highlighted.

Long-range transport of SO2 over South Africa: A case study of the Calbuco volcanic eruption in April 2015

South Africa (SA) is influenced by anthropogenic SO2 emissions, however, no studies exist detailing the atmospheric or climatic effects resulting from volcanic eruptions at this location. In this work, the Calbuco volcano situated in southern Chile (41.79°S, 73.13°W), was used as a case study to investigate the transport of volcanic SO2 plumes over SA in 2015. The Calbuco volcano underwent a series of eruptive phases starting from 22 April 2015. In this study, HYSPLIT forward trajectories and cluster analysis were carried out in order to estimate the altitude level of air parcels entering the SA subcontinent. These analyses along with Ozone Monitoring Instrument (OMI) assessments confirmed that trajectories beginning in Calbuco arrived in SA during the last few days of April (∼28 April 2015) and stayed until the 1st week of May 2015 (∼2 May 2015). In order to estimate the rise in SO2 columnar concentrations at various altitudes and surface SO2 levels, OMI data and 19 ground-based (GB) monitoring stations (situated throughout SA) were utilized. A comparative analysis employing SO2 data from pre-2014 and post-2016 volcanic eruption periods were also included in this study. Results showed that a major increase in SO2 concentration was observed at the Planetary Boundary Layer (∼ 2 DU) when compared to its upper layers during April and May 2015. Although some GB stations in North West province namely Damonsville, Mmabatho and Phola showed a moderate increase in SO2 values during April and May 2015, there was no significant rise in GB measurements at the surface in other locations However, we are not certain due to data gaps in the measurements and non-availability of ground-based measurements.

Temporal and spatial variation of particulate matter and its correlation with other criteria of air pollutants in Lanzhou, China, in spring-summer periods

The present study on major atmospheric pollutants is based on data received from 5 ground-based air-quality monitoring stations and satellite sounding which provides for the use of Moderate Resolution Imaging Spectroradiometer (MODIS) to study aerosol optical depth on the territory of the city of Lanzhou, Gansu Province. Data were received in the spring-summer period of 2014–2017. The present paper is intended to analyze the mass concentrations and spatial-temporal variation of PM 2.5 , PM 10 , CO, SO 2 , NO 2 and O 3 in the above city. When comparing and averaging the daily, monthly, and annual pollutants concentrations, it was revealed that the average PM 2.5 and PM 10 concentrations exceeded the Chinese Ambient Air Quality Standards (CAAQS) and were 42.37 and 112.67 μg/m 3 , respectively. Of all studied days, 62.5% showed high PM 2.5 concentration, and 78.2% - high PM 10 concentration. PM 2.5 and PM 10 demonstrated a strong correlation (R 2 = 0.81) with PM 2.5 being about 37% of PM 10 . This indicates that decrease of PM 10 emissions is very critical for reducing pollution with PM and thus for improving the air quality in China. The linear correlation analysis reveals unstable correlation between PM and gaseous pollutants, average correlation between PM and SO 2 (PM 2.5 /SO 2 r = 0.439; PM 10 /SO 2 r = 0.328), negative correlation between PM, SO 2 , NO 2 , CO and O 3 (PM 2.5 /O 3 r = −0.335, PM 10 /O 3 r = −0.213, SO 2 /O 3 r = −0.527, NO 2 /O 3 r = −0.272, CO/O 3 r = −0.558), and strong correlation between other gaseous pollutants (NO 2 /CO r = 0.502, NO 2 /SO 2 r = 0.534, SO 2 /CO r = 0.706). • Algorithm developed to identify active dust emission from HYSPLIT and CALIPSO. • To determine the level of aerosol pollution using satellite data MODIS. • The most powerful sources dust fraction were the territories of China and Mongolia (Taklimakan and Gobi Desert).

OMI and Ground-Based In-Situ Tropospheric Nitrogen Dioxide Observations over Several Important European Cities during 2005-2014.

In this work we present the evolution of tropospheric nitrogen dioxide (NO2) content over several important European cities during 2005–2014 using space observations and ground-based in-situ measurements. The NO2 content was derived using the daily observations provided by the Ozone Monitoring Instrument (OMI), while the NO2 volume mixing ratio measurements were obtained from the European Environment Agency (EEA) air quality monitoring stations database. The European cities selected are: Athens (37.98° N, 23.72° E), Berlin (52.51° N, 13.41° E), Bucharest (44.43° N, 26.10° E), Madrid (40.38° N, 3.71° W), Lisbon (38.71° N, 9.13° W), Paris (48.85° N, 2.35° E), Rome (41.9° N, 12.50° E), and Rotterdam (51.91° N, 4.46° E). We show that OMI NO2 tropospheric column data can be used to assess the evolution of NO2 over important European cities. According to the statistical analysis, using the seasonal variation, we found good correlations (R > 0.50) between OMI and ground-based in-situ observations for all of the cities presented in this work. Highest correlation coefficients (R > 0.80) between ground-based monitoring stations and OMI observations were calculated for the cities of Berlin, Madrid, and Rome. Both types of observations, in-situ and remote sensing, show an NO2 negative trend for all of locations presented in this study.

A simplified Suomi NPP VIIRS dust detection algorithm

Due to the complex characteristics of dust and sparse ground-based monitoring stations, dust monitoring is facing severe challenges, especially in dust storm-prone areas. Aim at constructing a high-precision dust storm detection model, a pixel database, consisted of dusts over a variety of typical feature types such as cloud, vegetation, Gobi and ice/snow, was constructed, and their distributions of reflectance and Brightness Temperatures (BT) were analysed, based on which, a new Simplified Dust Detection Algorithm (SDDA) for the Suomi National Polar-Orbiting Partnership Visible infrared Imaging Radiometer (NPP VIIRS) is proposed. NPP VIIRS images covering the northern China and Mongolian regions, where features serious dust storms, were selected to perform the dust detection experiments. The monitoring results were compared with the true colour composite images, and results showed that most of the dust areas can be accurately detected, except for fragmented thin dusts over bright surfaces. The dust ground-based measurements obtained from the Meteorological Information Comprehensive Analysis and Process System (MICAPS) and the Ozone Monitoring Instrument Aerosol Index (OMI AI) products were selected for comparison purposes. Results showed that the dust monitoring results agreed well in the spatial distribution with OMI AI dust products and the MICAPS ground-measured data with an average high accuracy of 83.10%. The SDDA is relatively robust and can realize automatic monitoring for dust storms.

Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling

Satellite-based PM2.5 concentration estimation is growing as a popular solution to map the PM2.5 spatial distribution due to the insufficiency of ground-based monitoring stations. However, those applications usually suffer from the simple hypothesis that the influencing factors are linearly correlated with PM2.5 concentrations, though non-linear mechanisms indeed exist in their interactions. Taking the Beijing-Tianjin-Hebei (BTH) region in China as a case, this study developed a generalized additive modeling (GAM) method for satellite-based PM2.5 concentration mapping. In this process, the linear and non-linear relationships between PM2.5 variation and associated contributing factors, such as the aerosol optical depth (AOD), industrial sources, land use type, road network, and meteorological variables, were comprehensively considered. The reliability of the GAM models was validated by comparison with typical linear land use regression (LUR) models. Results show that GAM modeling outperforms LUR modeling at both the annual and seasonal scale, with obvious higher model fitting-based adjusted R2 and lower RMSEs. This is confirmed by the cross-validation-based adjusted R2 with values of GAM-based spring, summer, autumn, winter, and annual models, which are 0.92, 0.78, 0.87, 0.85, and 0.90, respectively, while those of LUR models are 0.87, 0.71, 0.84, 0.84, and 0.85, respectively. Different to the LUR-based hypothesis of the “straight line” relations, the “smoothed curves” from GAM-based apportionment analysis reveals that factors contributing to PM2.5 variation are unstable with the alternate linear and non-linear relations. The GAM model-based PM2.5 concentration surfaces clearly demonstrate their superiority in disclosing the heterogeneous PM2.5 concentrations to the discrete observations. It can be concluded that satellite-based PM2.5 concentration mapping could be greatly improved by GAM modeling given its simultaneous considerations of the linear and non-linear influencing mechanisms of PM2.5.

Atmospheric mercury concentrations observed at ground-based monitoring sites globally distributed in the framework of the GMOS network.

Long-term monitoring of data of ambient mercury (Hg) on a global scale to assess its emission, transport, atmospheric chemistry, and deposition processes is vital to understanding the impact of Hg pollution on the environment. The Global Mercury Observation System (GMOS) project was funded by the European Commission (http://www.gmos.eu) and started in November 2010 with the overall goal to develop a coordinated global observing system to monitor Hg on a global scale, including a large network of ground-based monitoring stations, ad hoc periodic oceanographic cruises and measurement flights in the lower and upper troposphere as well as in the lower stratosphere. To date, more than 40 ground-based monitoring sites constitute the global network covering many regions where little to no observational data were available before GMOS. This work presents atmospheric Hg concentrations recorded worldwide in the framework of the GMOS project (2010-2015), analyzing Hg measurement results in terms of temporal trends, seasonality and comparability within the network. Major findings highlighted in this paper include a clear gradient of Hg concentrations between the Northern and Southern hemispheres, confirming that the gradient observed is mostly driven by local and regional sources, which can be anthropogenic, natural or a combination of both.

Spaceborne observations of low surface aerosol concentrations in the Stockholm region

This article investigates the feasibility of using spaceborne observations of aerosol optical thickness (AOT) derived with the Moderate Resolution Imaging Spectroradiometer (MODIS) for monitoring of fine particulate matter (PM2.5) in an environment of low aerosol loading. Previous studies of the AOT-to-PM2.5 relationship benefit from the large range of observed values. The Stockholm region features a comprehensive network of ground-based monitoring stations that generally show PM2.5 values <20 µg m−3. MODIS AOT at 555 nm is usually <0.20 and in good agreement with ground-based sun photometer observations in this region. We use MODIS Collection 5 AOT data with a horizontal resolution of 10 km×10 km and ground-based in-situ PM2.5 observations to derive an AOT-to-PM2.5 relationship that can be used to estimate fields of PM2.5. This has been carried out with respect to the months from April to September of the period 2000–2013. Relative average absolute deviations of 33–55 % (mean of 45 %) are obtained between MODIS-retrieved and ground-based PM2.5. The root mean square error is 0.2159 µg m−3 between retrieved and measured PM2.5. From spaceborne lidar observations, it is found that elevated aerosol layers are generally sparse in the Stockholm region. This favours remote sensing of PM2.5 from space. The deviations found between measured and retrieved PM2.5 are mainly attributed to infrequent situations of inhomogeneous aerosol layering for which column-integrated observations cannot be connected to surface conditions. Using MODIS Collection 6 data with a resolution of 3 km×3 km in a case study actually gives far fewer results than the coarser Collection 5 product. This is explained by the complex geography of the Stockholm region with a coastline and an abundance of lakes, which seems to induce biases in the retrieval of AOT at higher spatial resolution.

Gas-phase evaluation of the online NMMB/BSC-CTM model over Europe for 2010 in the framework of the AQMEII-Phase2 project

The Air Quality Model Evaluation International Initiative Phase2 aims to intercompare online coupled regional-scale models over North America and Europe. The NMMB/BSC Chemical Transport Model (NMMB/BSC-CTM) is a fully online integrated system for meso- to global-scale applications under development at the Barcelona Supercomputing Center. The NMMB/BSC-CTM is applied to Europe for the year 2010 in the framework of the AQMEII-Phase2 intercomparison exercise. This paper presents a spatial, temporal and vertical evaluation of the gas-phase model results. This is the first time that the model has been evaluated on a regional scale over a whole annual cycle. The model is compared with available ground-based monitoring stations for relevant reactive gases, ozonesondes, and OMI and MOPITT satellite retrievals of NO2 and CO. A comparative analysis of the present results and several European model evaluations is also presented here.The seasonal cycle for O3, NO2, SO2 and CO is successfully reproduced by the model. The O3 daily mean and daily maximum correlations for the analysed period are r = 0.68 and r = 0.75, respectively. The OMI tropospheric NO2 column retrievals are well reproduced, capturing the most polluted areas over Europe throughout the whole year. Modelled SO2 and CO surface concentrations are generally underestimated, especially during the winter months. Two different vertical configurations of the model (24 and 48 vertical layers) are also analysed. Although model results are very similar, the simulation configured with 48 vertical layers provides better results regarding surface O3 concentrations during summer. Compared to previous model evaluations, the NMMB/BSC-CTM's performance corresponds to state-of-the-art regional air quality models.

Air quality evaluation on an urban scale based on MODIS satellite images

Earth-observing satellites have provided satellite image datasets for urban-scale air quality monitoring. Aerosol optical thickness (AOT) at 1km resolution is retrieved from MODIS on Terra Satellite by using the improved algorithm. The 1km AOT data are validated by using AOT measurements from two AERONET stations and the 10km AOT products from MOD04_L2 in the period of October 2004. Then the validated 1km AOT data are compared with ground-based PM2.5 mass concentration in Beijing. Four empirical models which are used to investigate the relationship between AOT data and PM2.5 mass concentration are obtained by regression analysis and their correlations are R2=0.818, R2=0.750, R2=0.699 and R2=0.629 respectively. In order to verify the models, we use these models to retrieve PM2.5 concentration from MODIS AOT on the 11th of October 2012 and then compare it with the PM2.5 concentration from the ground measurements in that day. 50%, 46.4%, 46.4% and 39.3% of the stations are within the expected errors respectively by the four models. Preliminary analysis of these four models indicates that the quadratic model has significant potential to enhance air quality monitoring on an urban scale. Although the limited daily MODIS AOT data, variability of terrain, weather conditions, and many other factors can limit the ability of predicting PM2.5 concentration, the models provide a cost-effective approach for obtaining the distribution of PM2.5 information from satellite image, which complement the defects of the limited ground-based monitoring station measurements.