Natural heritage (NH) includes natural features that can be described as outstanding universal value at a national level. It refers to the importance of ecosystems, biodiversity, and geodiversity for their existence value, and the ecosystems can be considered as the spatial units for its mapping and assessment. The ecosystem services (ES) concept provides an appropriate basis in the form of assessment and mapping methods that enable linking the state of ecosystems with human well-being. Thus, it can be used as a platform to find solutions to the problems related to the conflicts between conservation and the use of the NH. In this paper, we aim to present the process of developing a methodological framework for mapping and assessment of ecosystem services provided by the natural heritage in Bulgaria for recreation and tourism. The conceptual framework of the ecosystem-based assessment of NH in Bulgaria is based on the assumption that the generation of NH for the needs of tourism can be presented as the linkages between the natural systems and tourism in the form of ES potential, flow, and demand. The results demonstrate that the NH can be presented as a spatial phenomenon conceptualized by the flows of benefits from ecosystems to people which contribute to human well-being. The mapping and assessment procedures are fully developed for application at a national level, while for the regional and local level, few pilot studies mark some basic foundations for further development.

The rich and diverse Natural Heritage of Bulgaria is a prerequisite for the development of nature- based tourism (NBT) of a new type. The research is carried out by the implementation of the ecosystem approach. The results include an assessment of the natural heritage capacity to provide goods and services for the development of NBT in the Tourist Regions (TR) of Bulgaria. The results show the spatial distribution of the natural heritage sites in all nine TR in Bulgaria and their natural capacity for development of different types of NBT. There are only 37 municipalities out of 265 with not a one Natural Heritage (NH) site, and all the rest have natural resources to develop NBT. The results can be of use for the achievement of the goals for sustainable tourism by assessment of the capacity to provide recreation ecosystem services (RES).

This study aims to reveal the arsenic dynamics in groundwater of а river floodplain contaminated with mine tailings under temperate climate conditions and natural river hydrodynamics. Arsenic concentrations were monitored in the primary morphological units of the floodplain in the upper stretch of the Ogosta River in NW Bulgaria. Iron, lead-silver, and gold mining heavily affected the river valley in the second half of the 20th century. We used groundwater monitoring data from 21 piezometers for the period 2016-2020. Based on the geochemical and geomorphological conditions in the valley, the piezometers were grouped into three clusters. Regression models were developed for each cluster and representative piezometers to predict arsenic concentrations. In the active floodplain, seasonal fluctuations in arsenic concentrations followed the river and groundwater regime. In this part of the valley floor, we determined two periods of elevated arsenic concentrations during the spring and autumn/winter seasons that coincide with high river water stages. Arsenic content in the groundwater of the higher floodplain was less dependent on the water level fluctuations but followed changes in redox potential, electrical conductivity, and water temperature. The obtained results showed the elaborated models as valuable tools for studying arsenic dynamics in alluvial aquifers of contaminated river floodplains. The suggested models could be coupled with groundwater monitoring systems to monitor arsenic concentrations and identify periods of the year with levels below and above threshold values.

This paper discusses the territorial organization of the chemical industry in Bulgaria. Using the ESRI ArcGIS software and applying cluster analysis, the study aims to group (cluster) the 28 Bulgarian districts (NUTS 3 level classification) based on produced output, persons employed, and Bulgarian lev (BGN) equivalent of foreign exchange earnings from exports for the period 2010-2020. Three reference years, 2010, 2015, and 2020, have been selected for the observed period. The general conclusion is that the chemical industry in Bulgaria is characterized by high territorial concentration. Varna was the leading district in developing the chemical industry in the observed period from 2010 to 2020, followed by Plovdiv, Ruse, and Sofia (the capital). At the other pole were the districts of Vidin, Montana, Vratsa, Pleven, Lovech, Razgrad, Silistra, Targoviste, Dobrich, Pernik, Kyustendil, Blagoevgrad, Sliven, Yambol, and Kardzhali. The findings of the research show that territorial polarization is linked with several factors that can be grouped according to their impact into four groups: 1) raw material and energy, 2) transport infrastructure and proximity to the end user, 3) state and environmental regulations, and 4) provision of skilled labor.

Light industry is one of the most important and priority industries in Bulgarian economy. It includes the production of textiles, clothing, and leather. Its development affects the state of the country’s overall economy. Despite the numerous studies that use GIS, in Bulgaria there have been no publications on the application of statistical analysis with the use of ArcGIS software. This study aims to apply Geographic cluster analysis using ArcGIS software to analyze the light industry in Bulgaria as of 2010, 2015, and 2020. The grouping of areas by selected indicators in the present study was performed with the Grouping Analysis tool. NO_SPATIAL_CONSTRAINT was selected for the Spatial Constraints parameter and FIND_SEED_LOCATIONS – for the Initialization Method. In this case, we used the K-Means algorithm to partition features into groups. That algorithm is one of the most popular and widely used clustering algorithms in GIS applications. The areas were grouped into 10 clusters. The selection of indicators on which the clustering procedure was based, is following the generally accepted indicators for assessing the state and importance of the food industry in the structure of the economy. The following indicators were used: output for 2010, 2015, and 2020; number of employees and export earnings as of 2010, 2015, and 2020, for each administrative-territorial unit. The spatial distribution of the population, in combination with the historical and the modern economic development of the settlements, forms the regional differences in the development of the light industry in the country. The cluster analysis of certain indicators for the assessment of the light industry at the NUTS 3 level as of 2010, 2015, and 2020, shows some changes in the spatial development trends of the industry. The cluster analysis shows that there are slight spatial differences in production at the NUTS 3 level, with large consumer centers and markets being the most important.