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Coastal bathing water, as a common good, is an economic resource of public health interest. Predictive models of coastal bathing water contamination are needed for timely prevention of pollution, warning of bathers, and activation of municipal services and utilities in case of contingencies, as well as institutional mechanism designs for common good management purposes. The goal of this research is to identify the variables that would improve predictive models of coastal bathing water bacterial contamination. The microbiological quality of coastal bathing water is affected by many variables. This research is an analysis of the following determinants: precipitation amount, seawater temperature and salinity, as well as few indicators of anthropogenic pressure on the environment such as registered population, registered tourist overnight stays and the amount of generated municipal waste, all possibly directly or indirectly affecting the bathing water quality in 17 coastal municipalities in the Primorje-Gorski Kotar County. The analysis showed that rainfall, as an instrumental confounder variable, influences salinity and seawater temperature by increasing groundwater discharge and bringing contamination i.e. increasing enterococci and Escherichia coli concentrations in coastal bathing water. Population as the conjectured independent variable, representing the anthropogenic cause of pollution, was once again falsified as a statistically significant determinant. For further research, longer-term sampling (preferably year-round) at micro-locations of comparable hydrogeological characteristics is recommended.

The most important natural and anthropogenic factors affecting stream water and groundwater chemistry in the Carpathians were identified mainly on the basis of hydrochemical case studies from numerous small headwater Carpathian catchments. The most important natural factor determining water chemistry in the Carpathians is geology. In areas formed of poorly soluble granite and gneiss rocks (crystalline portion of the Tatra Mountains), the total dissolved solids (TDS) and concentration of main cations and anions are many times lower than that in areas formed of highly soluble carbonate rocks (sedimentary portion of the Tatra Mountains, Pieniny Mountains) and also clastic rocks forming so-called Carpathian flysch (Beskidy Mountains, Carpathian Foothills). Stream water and spring water chemistry in the Carpathians change primarily due to hydrologic factors—changes in discharge. As discharge increases, TDS declines and the concentrations of most main ions also decline, while the concentration of K+, NO3−, and PO43− increases. Elevation and geographic location of springs and streams in given climate zone and vegetation zone are additional natural factors affecting water chemistry, and this is particularly true of SO42− and Cl− concentration as well as pH. Anthropogenic factors affecting water chemistry in the Carpathians include acid rain, deforestation, agriculture, and tourist-generated wastewater. The effects of acid rain are felt in the form of low concentrations of main cations in stream and spring water in the western part of the Carpathians. This region was affected by very high acidic sulfur and nitrogen deposition in the second half of the twentieth century. Deforestation in the Carpathians impacts mainly spruce monocultures declining due to acid rain, strong winds, and bark beetle infestation, and is the main cause of increasing NO3− concentrations in stream water and spring water. Agricultural land use does not threaten stream water and groundwater due to low usage of mineral fertilizer. Threats that do exist are associated with unregulated releases of wastewater in rural areas. Wastewater generated by tourists is a major threat to stream water quality in areas with a high environmental value that are highly popular with tourists. Tourist lodges release wastewater into Carpathian streams leading to excess nitrogen and phosphorus concentration in stream water.

Urban waterlogging disasters are affected by environmental conditions and human activities. Previous studies had explored the effect of land-use type on waterlogging in relatively small watersheds. Few, however, have comprehensively revealed the relative contributions of the environmental and anthropogenic factors to urban waterlogging concerning different scales of analysis. Indeed what is less known, are the dominant factors and the appropriate scale of analysis. To overcome this limitation, a novel method that integrates the stepwise regression model with hierarchical partitioning analysis is presented. The purpose is to investigate the complex mechanism of urban waterlogging by identifying the relative contribution of each environmental and anthropogenic factor and the stability linking waterlogging to influencing factors at multiple scales of analysis (i.e. 1 km, 2 km, 3 km, 4 km, and 5 km). We consider waterlogging events in the central urban districts of Guangzhou (PR China) from 2009 to 2015 as a case study. The results show that the spatial distribution of waterlogging events in the central urban area presents a strong agglomeration pattern. The waterlogging hot spots are mainly concentrated in the historical area of Guangzhou. Under all analysis scales, we find that the percent cover of urban green spaces (44.74%), percent cover of residential area (41.03%), and slope.std (36.85%) both have a dominant contribution to urban waterlogging, which suggests the importance of land cover composition in determining urban waterlogging. However, the relative contribution and dominant factors of waterlogging varied across different analysis scales, presenting a strong scale effect. Under a small analysis scale (1 km), the topography factors (slope.std and relative elevation) are confirmed as the dominant variables; however, with the increase of analysis scale, the influence of land cover composition (greenspace, residence area, grassland) and land cover spatial configuration (LPI, AI, Cohesion index) on waterlogging magnitude is greater than other factors. This finding provides additional insights that the urban waterlogging can be alleviated by balancing the relative composition of land cover features as well as by optimizing their spatial configuration. Since the optimal statistical scale for urban waterlogging studies only worked for specific influencing factors, the appropriate analysis scale for urban waterlogging study should be determined by the characteristics of study areas. This study has the capability to extend our scientific understanding of the complex mechanisms of waterlogging in the highly urbanized coastal city, providing useful support for the prevention and management of urban waterlogging.