Traffic induced air pollution modeling: Scenario analysis for air quality management in street canyon

This study was supported by the Riga City research project DMV-17-60-lī-03.02.2017. The authors wish to thank the Riga City Council and Latvian Environment, Geology and Meteorology Centre for data used in this study.Mathematical models are intensively used in environmental science for various reasons - status quo assessment, statistical modeling, forecasting, and planning, scenario analysis. Traffic flow and related atmospheric pollution modeling is one of the most complex challenges because of various aspects, - wide versa of affecting factors, daily, diurnal, weekly, monthly and yearly variability and non-stability of them. In the present study atmospheric model OSPM (Operational Street Pollution Model) is used to calculate NOx and PM10 concentration levels in the historical center (street canyon) of the city of Riga (Latvia) in order to make further assumptions (e.g., for the traffic load) for minimizing traffic induced air pollution in street canyons. In total five different scenarios were analyzed involving prioritizations of public transport, restrictions for old private cars or flow limitations in traffic jam situations during working days.Institute of Solid State Physics, University of Latvia as the Center of Excellence has received funding from the European Union’s Horizon 2020 Framework Programme H2020-WIDESPREAD-01-2016-2017-TeamingPhase2 under grant agreement No. 739508, project CAMART

TRAFFIC FLOW HYPOTHETICAL MODELLING FOR AIR QUALITY IMPROVEMENT AND PLANNING PURPOSES

The main emphasis of this research was to describe air pollution level and dispersion in a typical street canyon (Valdemara Street in Riga (Latvia) city centre), afterward to postulate potential development scenarios and perform modelling in order to understand the influence on air pollution level. For this purpose special mathematical model was used - Operational Street Pollution Model (OSPM), which was developed by the National Environmental Research Institute in Denmark. Following development scenarios were tested: (1) realistic environmentally friendly - decrease of traffic flow by 50 %, as according to street interviews about 36 - 50 % of drivers are ready to change driving habits from car to bicycle; (2) strictly limited – “green light” for public transport, but restrictions for old private cars, flow speed limitations.  

Perspectives on Holy Springs as a Religious Tourism Resource: a Comparative Study of the Baltic States and India

Holy springs played an immense role in religious tourism globally over centuries. It is widely believed that visiting holy springs as part of religious practice symbolises the washing away of sins and enhances ones’ health condition and well-being. Both in India and the Baltic States, a significant number of holy springs have various religious significances. However, over a period of time, due to changes in socio-cultural and political conditions, the use of springs for religious purposes and health reasons has also changed. The aim of this article is to analyse the change in the use of holy springs as religious tourism resources in India and the Baltic States (Lithuania, Latvia, and Estonia). Both semi-structured interviews and content analysis are used as qualitative research approaches in the study. Semi-structured interviews in India and the Baltic States were undertaken with both tourists and spring management authorities on the motivations, changing aspects of the use of water springs, and possible solutions for the sustainable use of springs. The content analysis included written records, blogs, and social media sites, to establish historical perspectives on the use of water springs. In the conclusion, the authors provide suggestions for alternative ways of promoting spring tourism at religious sites

Assessing automated gap imputation of regional scale groundwater level data sets with typical gap patterns

Large groundwater level (GWL) data sets are often patchy with hydrographs containing continuous gaps and irregular measurement frequencies. However, most statistical time series analyses require regular observations, thus hydrographs with larger gaps are routinely excluded from further analysis despite the loss of coverage and representativity of an initially large data set. Missing values can be filled in with different imputation methods, yet the challenge is to assess the imputation performance of automated methods. Assessment of such methods tends to be carried out on randomly introduced missing values. However, large GWL data sets are commonly dominated by more complex patterns of missing values with longer contiguous gaps. This study presents a new artificial gap introduction approach (TGP- typical gap patterns) that improves our understanding of automated imputation performance by mimicking typical gap patterns found in regional scale groundwater hydrographs. Imputation performance of machine learning algorithm missForest and imputePCA is then compared with commonly applied linear interpolation to prepare a gapless daily GWL data set for the Baltic states (Estonia, Latvia, Lithuania). We observed that imputation performance varies among different gap patterns, and performance for all imputation algorithms declined when infilling previously unseen extremes and hydrographs influenced by groundwater abstraction. Further, missForest algorithm substantially outperformed other methods when infilling contiguous gaps (up to 2.5 years), while linear interpolation performs similarly for short random gaps. The TGP approach can be of use to assess the complexity of missing observation patterns in a data set and its value lies in assessing the performance of gap filling methods in a more realistic way. Thus the approach aids the appropriate selection of imputation methods, a task not limited to groundwater level time series alone. The study further provides insights into region-specific data peculiarities that can assist groundwater analysis and modelling

Spatial and temporal variability in summertime dissolved carbon dioxide and methane in temperate ponds and shallow lakes

Small waterbodies have potentially high greenhouse gas emissions relative to their small footprint on the landscape, although there is high uncertainty in model estimates. Scaling their carbon dioxide (CO2) and methane (CH4) exchange with the atmosphere remains challenging due to an incomplete understanding and characterization of spatial and temporal variability in CO2 and CH4. Here, we measured partial pressures of CO2 (pCO2) and CH4 (pCH4) across 30 ponds and shallow lakes during summer in temperate regions of Europe and North America. We sampled each waterbody in three locations at three times during the growing season, and tested which physical, chemical, and biological characteristics related to the means and variability of pCO2 and pCH4 in space and time. Summer means of pCO2 and pCH4 were inversely related to waterbody size and positively related to floating vegetative cover; pCO2 was also positively related to dissolved phosphorus. Temporal variability in partial pressure in both gases weas greater than spatial variability. Although sampling on a single date was likely to misestimate mean seasonal pCO2 by up to 26%, mean seasonal pCH4 could be misestimated by up to 64.5%. Shallower systems displayed the most temporal variability in pCH4 and waterbodies with more vegetation cover had lower temporal variability. Inland waters remain one of the most uncertain components of the global carbon budget; understanding spatial and temporal variability will ultimately help us to constrain our estimates and inform research priorities

Indicative value and training set of freshwater organic-walled algal palynomorphs (non-pollen palynomorphs)

Only a few studies have related modern non-pollen palynomorphs to environmental variables, limiting the development of non-pollen palynomorphs training sets. Here, we perform substantial groundwork by developing a training set for freshwater organic-walled algal palynomorphs. We sampled surface sediments from 78 waterbodies across Latvia in north-eastern Europe with the aim of gaining infor-mation on the distribution and diversity of algal palynomorphs. We analysed the preferred living con-ditions (water and sediment properties) of algal palynomorphs in conjunction with climate and catchment characteristics (Quaternary sediment type, landscape usage, and composition). In total, 94 species/taxa belonging to four phyla (Cyanobacteria, Chlorophyta, Charophyta, and Ochrophyta) were identified. By applying statistical and descriptive analyses, we showed the indicative value of algal palynomorphs along various gradients. Using the established training set, we for the first constructed organic-walled algal palynomorphs-based pH and electric conductivity reconstructions for two lakes where natural and anthropogenic variability was recognised. Freshwater algal palynomorphs assem-blages constrained by environmental and climatic variables open new horizons for further non-pollen palynomorphs qualitative and quantitative research. (c) 2022 Elsevier Ltd. All rights reserved.Peer reviewe