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In this paper we studied the temporal evolution of the Planetary Boundary Layer height (PBLH) over the basin of Athens, Greece during a 5-year period (2011-2016) using data from the EOLE Raman lidar system. The lidar data (range-corrected lidar signals-RCS) were selected around 12:00 UTC and 00:00 UTC for a total of 332 cases: 165 days and 167 nights. Extended Kalman filtering techniques were used for the determination of the PBLH. Moreover, several well established techniques for the PBLH estimation based on lidar data were also tested for a total of 35 cases. Comparisons with the PBLH values derived from radiosonde data were also performed. The mean PBLH over Athens was found to be of the order of 1617±324 m at 12:00 UTC and of 892±130 m at 00:00 UTC, for the period examined. The mean PBLH growth rate was found to be about 170±64 m h-1 and 90±17 m h-1, during daytime and nighttime, respectively.

Ground monitoring station data of PM2.5 are not available for each day and all places in urban areas. In this research, taking Tehran as an example, a geographically and temporally weighted regression (GTWR) model was utilized to investigate the spatial and temporal variability relationship between PM2.5 concentrations measured at ground monitoring stations and satellite aerosol optical depth (AOD) data. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor produced AOD values with 3-km spatial resolution. Using meteorological variables and land use information as additional predictors utilized in the GTWR model, the AOD was converted to PM2.5 at ground level for warm (October to March) and cold seasons (April to September) from 2011 to 2017. To improve the accuracy of the correlation coefficient between converted PM2.5 from the GTWR model and PM2.5 concentrations measured at ground monitoring station, the results of a linear model (LR), a nonlinear model (artificial neural network (ANN)), and hybrid models including general regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) were compared. The results of the linear, nonlinear, and hybrid models for the cold season display higher accuracy compared with the results for the warm season. Among the used models, the GRNN model has higher accuracy compared with the other models. This study reveals that AOD conversion to particulate matter by the GTWR model and its simulation to PM2.5 at ground level using a hybrid model such as the GRNN can be used to study air quality. [ABSTRACT FROM AUTHOR]

For more than 10 years now the National and Kapodistrian University of Athens, Greece, contributes to the UN/ECE ICP Materials programme for monitoring of the corrosion/soiling levels of different kind of materials due to environmental air-quality parameters. In this paper we present the results obtained from the analysis of such observational data that were collected in Athens during the period 2003-2012. According to these results the corrosion/soiling of the particular exposed materials tend to decrease over the years, except for the case of copper. Based on this long experimental database applicable to multi-pollutant situation of the Athens basin we present dose response functions (DRFs) considering, that 'dose' stands for the air pollutant concentration, 'response' for the material mass loss (normally per annum) and the 'function' the relationship derived by the best statistical fit to the data. [ABSTRACT FROM AUTHOR]

During the summer of 2017, multiple huge fires occurred on Mount Vesuvius (Italy), dispersing a large quantity of ash in the surrounding area ensuing the burning of tens of hectares of Mediterranean scrub. The fires affected a very large area of the Vesuvius National Park and the smoke was driven by winds towards the city of Naples, causing daily peak values of particulate matter (PM) concentrations at ground level higher than the limit of the EU air quality directive. The smoke plume spreading over the area of Naples in this period was characterized by active (lidar) and passive (sun photometer) remote sensing as well as near-surface (optical particle counter) observational techniques. The measurements allowed us to follow both the PM variation at ground level and the vertical profile of fresh biomass burning aerosol as well as to analyze the optical and microphysical properties. The results evidenced the presence of a layer of fine mode aerosol with large mean values of optical depth (AOD > 0.25) and Ångstrom exponent (γ > 1.5) above the observational site. Moreover, the lidar ratio and aerosol linear depolarization obtained from the lidar observations were about 40 sr and 4%, respectively, consistent with the presence of biomass burning aerosol in the atmosphere. [ABSTRACT FROM AUTHOR]

Particulate matter smaller than 10 μm (PM10) is an important air pollutant that adversely affects human health by increasing the risk of respiratory and cardiovascular diseases. Recent studies reported multiple extreme PM10 levels at high altitude Peruvian cities, which resulted from a combination of high emissions and limited atmospheric circulation at high altitude. However, the emission sources of the PM10 still remain unclear. In this study, we collected PM10 samples from four sites (one industrial site, one urban site, and two rural sites) at the city of Arequipa, Peru, during the period of February 2018 to December 2018. To identify the origins of PM10 at each site and the spatial distribution of PM10 emission sources, we analyzed major and trace element concentrations of the PM10. Of the observed daily PM10 concentrations at Arequipa during our sampling period, 91% exceeded the World Health Organization (WHO) 24-h mean PM10 guideline value, suggesting the elevated PM10 strongly affected the air quality at Arequipa. The concentrations of major elements, Na, K, Mg, Ca, Fe, and Al, were high and showed little variation, suggesting that mineral dust was a major component of the PM10 at all the sites. Some trace elements, such as Mn and Mo, originated from the mineral dust, while other trace elements, including Pb, Sr, Cu, Ba, Ni, As and V, were from additional anthropogenic sources. The industrial activities at Rio Seco, the industrial site, contributed to significant Pb, Cu, and possibly Sr emissions. At two rural sites, Tingo Grande and Yarabamba, strong Cu emissions were observed, which were likely associated with mining activities. Ni, V, and As were attributed to fossil fuel combustion emissions, which were strongest at the Avenida Independencia urban site. Elevated Ba and Cu concentrations were also observed at the urban site, which were likely caused by heavy traffic in the city and vehicle brake wear emissions. [ABSTRACT FROM AUTHOR]

Transportation sources are a major contributor to air pollution in urban areas, and the role of air quality modeling is vital in the formulation of air pollution control and management strategies. Many models have appeared in the literature to estimate near-field ground level concentrations from mobile sources moving on a highway. However, current models do not account explicitly for the effect of wind shear (magnitude) near the ground while computing the ground level concentrations near highways from mobile sources. This study presents an analytical model (SLINE 1.0) based on the solution of the convective–diffusion equation by incorporating the wind shear near the ground for gaseous pollutants. The dispersion coefficients for stable and unstable atmospheric conditions are based on the near-field parameterization. Initial vertical dispersion coefficient due to the wake effect of mobile sources is incorporated based on a literature review. The model inputs include emission factor, wind speed, wind direction, turbulence parameters, and terrain features. The model is evaluated based on the Idaho Falls field study (2008). The performance of the model is evaluated using several statistical parameters. Results indicate that the model performs well against this dataset in predicting concentrations under both the stable and unstable atmospheric conditions. The sensitivity of the model to compute ground-level concentrations for different inputs is presented for three different downwind distances. In general, the model shows Type III sensitivity (i.e., the errors in the input will show a corresponding change in the computed ground level concentrations) for most of the input variables using the ASTM (American Society for Testing and Materials) method. However, some recalibration of the model constants is needed using several field datasets to make sure that the model is acceptable for computing ground-level concentrations in engineering applications. [ABSTRACT FROM AUTHOR]

With the current COVID-19 pandemic being spread all over the world, lockdown measures are being implemented, making air pollution levels go down in several countries. In this context, the air quality changes in the highly populated and trafficked Brazilian states of São Paulo (SP) and Rio de Janeiro (RJ) were addressed using a combination of satellite and ground-based daily data analysis. We explored nitrogen dioxide (NO 2 ) and fine particulate matter (PM 2.5 ) daily levels for the month of May from 2015–2020. Daily measurements of NO 2 column concentrations from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura satellite were analyzed and decreases of 42% and 49.6% were found for SP and RJ, respectively, during the year 2020 compared to the 2015–2019 average. Besides NO 2 column retrievals, ground-based data measured by the Brazilian States Environmental Institutions were analyzed and correlated with satellite retrievals. Correlation coefficients between year-to-year changes in satellite column and ground-based concentrations were 77% and 53% in SP and RJ, respectively. Ground-based data showed 13.3% and 18.8% decrease in NO 2 levels for SP and RJ, respectively, in 2020 compared to 2019. In SP, no significant change in PM 2.5 was observed in 2020 compared to 2019. To further isolate the effect of emissions reduction due to the lockdown, meteorological data and number of wildfire hotspots were analyzed. NO 2 concentrations showed negative and positive correlations with wind speed and temperature, respectively. PM 2.5 concentration distributions suggested an influence by the wildfires in the southeast region of the country. Synergistic analyses of satellite retrievals, surface level concentrations, and weather data provide a more complete picture of changes to pollutant levels. [ABSTRACT FROM AUTHOR]

Due to the exponential growth of the SARS-CoV-2 pandemic in Spain (2020), the Spanish Government adopted lockdown measures as mitigating strategies to reduce the spread of the pandemic from 14 March. In this paper, we report the results of the change in air quality at two Atlantic Coastal European cities (Northwest Spain) during five lockdown weeks. The temporal evolution of gaseous (nitrogen oxides, comprising NOx, NO, and NO2; sulfur dioxide, SO2; carbon monoxide, CO; and ozone, O3) and particulate matter (PM10; PM2.5; and equivalent black carbon, eBC) pollutants were recorded before (7 February to 13 March 2020) and during the first five lockdown weeks (14 March to 20 April 2020) at seven air quality monitoring stations (urban background, traffic, and industrial) in the cities of A Coruña and Vigo. The influences of the backward trajectories and meteorological parameters on air pollutant concentrations were considered during the studied period. The temporal trends indicate that the concentrations of almost all species steadily decreased during the lockdown period with statistical significance, with respect to the pre-lockdown period. In this context, great reductions were observed for pollutants related mainly to fossil fuel combustion, road traffic, and shipping emissions (−38 to −78% for NO, −22 to −69% for NO2, −26 to −75% for NOx, −3 to −77% for SO2, −21% for CO, −25 to −49% for PM10, −10 to −38% for PM2.5, and −29 to −51% for eBC). Conversely, O3 concentrations increased from +5 to +16%. Finally, pollutant concentration data for 14 March to 20 April of 2020 were compared with those of the previous two years. The results show that the overall air pollutants levels were higher during 2018–2019 than during the lockdown period. [ABSTRACT FROM AUTHOR]

Similarly to other countries, the first wave of the COVID pandemic induced a collapse of mobility in Hungary during the spring of 2020. From the environmental perspective, the obtained road traffic reduction of 20–50% could be regarded as an undesired traffic regulation experiment. Air quality impacts within Hungary were evaluated based on data from 52 monitoring sites measuring concentrations of pollutants NOx, O3, and PM10. Air pollution during the lockdown was compared to the same period (February–June) in the reference years 2014–2019. The large spatial heterogeneity of the air quality response was explored. The emission reduction coincided with the extreme weather of 2020, characterized by unusually warm pre-lockdown February and spring drought. The anomalously low pre-lockdown air pollution was further reduced (NOx) or increased (PM10) during the restrictions. Compared to the previous years, NOx concentrations during the curfew were found to differ between −4.1 and +0.2 standard deviations (median −1.55 SD), or −45% and +3% (median −18%) among different monitoring locations. Ozone concentrations were unusually high due to both weather and chemical reasons (median +11% or +0.8 SD), while the PM10 response was modest and largely weather-driven (median +7% or +0.4 SD). [ABSTRACT FROM AUTHOR]