Seventy-one percent of the earth surfaces is covered by oceans. Water therefore is an important habitat for microorganisms and the other living beings. A consistent microbial biodiversity is present in water from phototrophs to chemioorganotrophs. The complex relationships between different microorganisms and the environment are often modified by organic, chemical and physic contaminations. The input of organic material can determine pathogenic pollution. The presence of pathogens has to be monitored to eliminate serious problems for animal and human health. Water, in fact, can be a vehicle direct (drinking water) or indirect (irrigation water) for microbial pathogens | Il 71% della superficie terrestre è costituito dagli oceani. L'acqua pertanto è un importante ambiente per i microrganismi, oltre che per tutti gli altri esseri viventi. Una grande varietà di tipi microbici colonizzano l'habitat acquatico, dai fototrofi ai chemiorganotrofi. Le dinamiche che si creano fra i diversi componenti microbici e l'ambiente sono spesso alterate da contaminazioni organiche, chimiche e fisiche. L'immissione di materiale organico può anche essere fonte di inquinamento di microrganismi patogeni la cui presenza va monitorata al fine di evitare seri problemi alla salute umana e animale. L'acqua, infatti, può rappresentare un veicolo di trasferimento, sia diretto (acqua potabile), sia indiretto (acque di irrigazione), di microrganismi patogeni

Global aquatic ecosystems are essential to human existence and have deteriorated seriously in recent years. Understanding the influence mechanism of habitat variation on the structure of the food-web allows the effective recovery of the health of degraded ecosystems. Whereas most previous studies focused on the selection of driving habitat factors, the impact of habitat variation on the food-web structure was rarely studied, resulting in the low success rate of ecosystem restoration projects globally. This paper presents a framework for exploring the effects of spatial variations in water quality and hydrological habitat factors on the food-web structure in city waters. Indices for the evaluation of the food-web structure are first determined by integrating model-parameter extraction via literature refinement. The key water quality and hydrological factors are then determined by coupling canonical correspondence analysis with partial least squares regression. Their spatial variation is investigated using spatial autocorrelation. Finally, fuzzy clustering is applied to analyze the influence of the spatial variations in water quality and hydrological factors on the food-web structure. The results obtained in Ji'nan, the pilot city of water ecological civilization in China, show that the Shannon diversity index, connectance index, omnivory index, and the ratio of total primary production to the total respiration are important indicators of food-web structural change. They show that the driving factors affecting the aquatic food-web structure in Ji'nan are hydrological factors (e.g., river width, water depth, and stream flow), physical aspects of water quality (e.g., air temperature, water temperature, electrical conductivity, and transparency), and chemical aspects (e.g., potassium, dissolved oxygen, calcium, and total hardness). They also show that the stability of the food-web is more prone to spatial variations in water quality than in hydrological factors. Higher electrical conductivity, potassium, total hardness, and air temperature lead to deteriorated food-web structures, whereas better transparency improves structure and stability. We found that water and air temperature are the most important factors in the spatial variation of the food-web structure in the study area, followed by total hardness. Transparency is the least important factor. Large disparities and varied spatial distributions exist in the driving effects of water quality and hydrological factors across regions attributable to differences in geographical environments, water salinity (fresh vs. sea water), and environmental factors (e.g., water pollution). The above methods and results serve as a theoretical and scientific basis for a high success rate of aquatic ecosystem restoration projects in the study area and other cities worldwide.

The coupled heat-water model presented in paper I is used to calculate the temperature and water activity at the surface of an unwrapped lean beef meat sample subjected to changing temperature conditions. Variation in a(w) during heat treatment was determined from weight loss measurements. Surface temperature and water activity predicted by the coupled heat-water model agree with measurements, providing that the water diffusivity inside the product varies with the local water content.