Transportation sources are a major contributor to air pollution in urban areas. The role of air quality modelling 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 based on the solution of the convective-diffusion equation by incorporating the wind shear near the ground for gaseous pollutants. The model input includes emission rate, wind speed, wind direction, turbulence, and terrain features. The dispersion coefficients are based on the near field parameterization. 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. However, the model equations should be re-examined for three input variables (wind velocity at the reference height and two variables related to the vertical spread of the plume) to make sure that that the model is valid for computing ground level concentrations.

Water, in its various forms, is considered a key parameter in climate change studies. Water vapor is recognized as the most important natural greenhouse gas playing a vital role in the hydrological cycle. Thus, studying air humidity fluctuations may contribute towards a deeper understanding of the radiative and thermodynamic processes that take part in the Earth’s atmosphere. Traditional statistical analysis is not always efficient to describe complex physical processes with high temporal variability. In addition, a more thorough study of the variations of climatic parameters requires examination of their time series fluctuations over multiple time scales. Fractal theory offers robust solutions that satisfy the above requirements. In this work, the Multifractal Detrended Fluctuation Analysis (MF-DFA) is used in order to investigate the intrinsic dynamics of daily relative humidity time series over the Greek region from a nonlinear perspective. The scaling properties and the multifractal structure of the time series are studied by examining the fluctuation function, the multifractal spectrum and the Hurst exponent.

An important aspect in environmental sciences is the study of air quality, using statistical methods (environmental statistics) which utilize large datasets of climatic parameters. The air quality monitoring networks that operate in urban areas provide data of the most important pollutants, which via environmental statistics can be used for the development of continuous surfaces of pollutants concentrations. Generating ambient air quality maps can help guide policy makers and researchers to formulate measures to minimize the adverse effects. The information needed for a mapping application can be obtained by employing spatial interpolation methods to the available data, for generating estimations of air quality distributions. This study uses point monitoring data from the network of stations that operates in Athens. A machine learning scheme will be applied as a method to spatially estimate pollutants’ concentrations and the results can be effectively used to implement missing values and provide representative data for statistical analyses purposes.

Abstract: This study presents how feedlot dust size and composition contribute to atmospheric ice nucleation and formation of local cloud and precipitation in the Texas Panhandle. Our previous work using Raman micro-spectroscopy revealed that ambient dust sampled at a commercial feedlot is predominantly composed of brown or black carbon, hydrophobic humic acid, water soluble organics, less soluble fatty acids and those carbonaceous materials mixed with salts and minerals (Hiranuma et al., 2011). Organic acids (i.e., long-chain fatty acids) and heat stable organics are recently found to be acting as an efficient ice-nucleating particle (INP; DeMott et al., 2018; Perkins et al., 2020). However, our knowledge regarding what particulate features of feedlot dust trigger immersion freezing in heterogeneous freezing temperatures (i.e., size vs. composition) is still lacking. To improve our knowledge, we conducted single particle physicochemical analyses of different types of feedlot dust simulants and their ice crystal residual (ICR) samples. Our preliminary results show that aerosol particle composition is dominated by organics with substantial inclusion of salts (e.g., potassium). This is consistent with the previous study of TXD particles composition analyses (Hiranuma et al., 2011). The elemental composition analysis revealed some notable difference between aerosol particle samples and residual samples, indicating the inclusion of non-hygroscopic organic particles as ice residuals. Our ICR analysis also revealed a decrease in hygroscopic salt inclusion in residuals, which may imply an importance of immersion rather than condensation freezing as agricultural INPs. Dry heat-resistant physicochemical properties and predominantly supermicron nature of feedlot-emitted INPs also highlight this study. Further research should focus on understanding how organic composition and/or other particulate properties influence ice nucleation. Such organic INP dataset has long been a missing piece in the study area of cloud microphysics and atmospheric chemistry and is of importance to improve atmospheric models of cloud feedbacks and determine their impact on the regional weather and climate.

References:

DeMott P. J. et al.: Environ. Sci.: Processes Impacts, 20, 1559–1569, 2018.

Hiranuma, N., et al.: Atmos. Chem. Phys., 11, 8809–8823, 2011.

Perkins, R. J. et al.: ACS Earth Space Chem., 4, 133–141, 2020.

Although the continued world urban population growth is responsible for the increasing anthropogenic CO₂ emissions, accurate accounting of the partitioning between urban anthropogenic and biospheric CO₂ signals is key to effective emission reduction strategies. Furthermore, the partitioning of urban anthropogenic and biospheric CO₂ emissions, estimated from ground-based atmospheric measurements can contribute to an independent reporting of local, regional and national CO₂ emission inventories. In this study, between the years 2017 to 2019, daily and seasonal ground-based cavity-ring down spectroscopic (CRDS) CO₂ measurements were recorded in Cookeville, a medium sized city located within the Eastern highland rim region of the United States (36.1628° N, 85.5016° W). The obtained CO₂ signals were partitioned into anthropogenic and biospheric dry mole fractions, utilizing CO as a tracer. The average winter biospheric CO₂ dry mole fraction values ranged from -0.65 ± 3.44 ppm to -9.80 ± 8.99 ppm. On the other hand, anthropogenic dry mole fraction CO₂ values varied from 10.01 ± 6.53 ppm to 22.88 ± 9.96 during the winter season. During the winter season, the percentage contribution of the oxidation reaction between the OH radical and isoprene (CH₂=C(CH₃)−CH=CH₂ + OH) to the total CO budget in Cookeville is negligible. However, during the summertime, the CO from isoprene oxidation was estimated to be significant, although less than 50%, implying that any summertime study based on the CO as a tracer of combustion emission should account for its photochemical production through biogenic volatile organic compounds (VOCs).

The temporal variability of solar shortwave radiation (SSR) was assessed over Northern Eurasia (40^o – 80^o N; 10^o W – 180^o E) by using SSR reconstruction model since the middle of 20 century. The reconstruction model estimates the year-to-year SSR variability as a sum of variations in SSR due to changes in aerosol, effective cloud amount and cloud optical thickness which are the most effective factors affecting SSR. The retrievals of year-to-year SSR variations according to different factors were tested against long-term measurements in Moscow State University Meteorological Observatory during 1968-2016. The reconstructed changes show a good agreement with measurements with determination factor R² = 0,8. The analysis of SSR trends since 1979 has detected a significant growth of 2.5% per decade, which may be explained by its increase due to change in cloud amount (+2.4% per decade) and aerosol optical thickness (+0.4% per decade). The trend due to cloud optical thickness was statistically insignificant. Using the SSR reconstruction model we obtained the long-term SSR variability due to different factors for the territory of Northern Eurasia. The increasing SSR trends have been detected on most sites since 1979. The long-term SSR variability over Northern Eurasia is effectively explained by changes in cloud amount and, in addition, by changes in aerosol loading over the polluted regions. The retrievals of the SSR variations showed a good agreement with the changes in global radiance measurements from World Radiation Data Centre (WRDC) archive. The work was supported by RFBR grant number 18-05-00700.

Studies have shown that maxima in the time series of Northern Hemisphere (NH) Integrated Enstrophy (IE) can be associated with large-scale flow regime transitions and often, the onset and decay of blocking events. Studies have also demonstrated that maxima of this quantity can be identified in ensemble model forecasts as much as 10 days in advance. During February and March 2019, strong IE maxima were associated with changes in the NH flow regimes that brought very cold conditions to the central part of the USA. These colder conditions also were associated with very strong Pacific and Atlantic Region blocking events. Using the NCEP re-analyses, three different teleconnection indexes, and surface temperature data from six different cities in the central USA, these IE maxima are identified. The maximum, minimum temperature and precipitation characteristics for these cities during the different large-scale flow regime characteristics are determined. The results will demonstrate that relatively warm conditions persisted through the first part of February before a period of anomalously colder (as much as 20^o F below normal) and drier weather, with more snow, persisted into early March. This period was bookended by major changes in the NH IE time series and a strong simultaneous NH blocking episode. Following this period, the temperature regime returned to values that were closer to normal. Finally, these changes were anticipated well by an ensemble model.

High horizontal resolution regional climate model simulations serve as forcing data for crop and dynamic vegetation models to generate possible scenarios for the future concerning the effects of climate change on crop yields and pollinators. Here, we performed convection-permitting hindcast simulations with the regional climate model COSMO5.0-CLM9 (CCLM) from 1980 to 2019 with a spin-up starting at 1979. The model was driven with hourly ERA5 data, which is the latest climate reanalysis product by ECMWF and directly downscaled to 3 km horizontal resolution over central Europe. The land-use classes are described by ECOCLIMAP, and the soil type and depth by HWSD. The evaluation is carried out in terms of temperature, precipitation, and extreme weather indices, comparing CCLM output with the gridded observational dataset HYRAS from the German Weather Service. While CCLM inherits a warm and dry summer bias found in its parent model, it reproduces the main features of the recent past climate of central Europe, including the seasonal mean climate patterns and probability density distributions. Furthermore, the model catches extreme weather events related to heat, drought, heavy rains, flooding, and spring frost events. The results highlight the possibility to directly downscale ERA5 data with regional climate models avoiding the multiple nesting approach and high computational costs. This study adds confidence to convection-permitting climate projections of future changes in agricultural extreme events.

The pollutant emissions from vehicles are forming major sources of metallic nanoparticles into the environment and surrounding atmosphere. In this research we spectrochemicaly analyse chemical composition of Particle Matter emissions from in-use Diesel engine passenger vehicles. We have extracted Diesel Particulate Matter from the end part of the tail pipe, from more than seventy different vehicles. And in laboratory we have used the high resolution laser induced plasma spectroscopy (LIBS) spectrochemical analytical technique to sensitively analyse chemical elements in different DPM. We have found that PM is composed of major, minor and trace chemical elements. The major compound of PM is not strictly Carbon element but rather other adsorbed metallic nanoparticles such as Iron, Chromium, Magnesium, Zinc, Calcium. Beside the major elements of DPM there are also minor elements: Silicon, Nickel, Titan, Potassium, Strontium, Molybdenium and others. Additionally in DPM are adsorbed atomic trace elements like Barium, Boron, Cobalt, Copper, Phosphorus, Manganese and Platinum. All these chemical elements are forming significant atomic composition of real PM from in-use Diesel engine vehicles.

We present our field results of ice-nucleating particle (INP) measurements by the commercialized version of Portable Ice Nucleation Experiment chamber (PINE) from two different campaigns. Our first field campaign ‘TxTEST’ was conducted at West Texas A&M University (July – August, 2019), and the other ‘ExINP-SGP’ campaign was held at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site (Oct – Nov, 2019). In both campaigns, PINE made semi-autonomous INP measurements at a high time resolution of 8 minutes for individual expansions with continuous temperature scans from -5 to -35°C in 90 minutes. The PINE instrument was set to have a minimum detection capability of ~0.3 INPs per liter of air. To complement our online PINE measurements, polycarbonate filter impactor and liquid impinger samples were also collected next to PINE. Offline-droplet freezing assays were later conducted from the filter and impinger samples for immersion freezing mode. Our preliminary results suggested that the immersion freezing was the dominant ice-nucleation mechanism at the SGP site compared to the deposition mode. We did not find any statistical correlationships between cloud condensation nuclei (CCN) and INP concentration during our ExINP-SGP period, suggesting that CCN activation is not a significant prerequisite for ice nucleation at the SGP site. In addition, we analyzed the relationship between various aerosol particle size ranges and INP abundance. At SGP, we found an increase in INPs with the super-micron particles, especially for diameters > 2 μm across the entire heterogeneous freezing temperature range examined by PINE. Lastly, we provide a variety of INP parameterizations, such as ice nucleation active surface site density, water activity based freezing, and cumulative INP per liter of air, representing the ambient INPs in SGP. Our field campaign results demonstrated the PINE’s ability of making remote INP measurements, promising the future long-term operations including at isolated locations.