Ecological risk assessment of multimedia hazardous air pollutants: estimating exposure and effects

Assessment of Soil Salinization Risk by Remote Sensing-Based Ecological Index (RSEI) in the Bosten Lake Watershed, Xinjiang in Northwest China

Accurate real-time information about the spatial and temporal dynamics of soil salinization is crucial for preventing the aggravation of salinization and achieving sustainable development of the ecological environment. With the Bosten Lake watershed as the study area, in this study, the regional risk factors of soil salinization were identified, the salinization information was extracted, and the remote sensing-based ecological index (RSEI) of soil salinization was assessed through the combined use of remote sensing (RS) and geographic information system (GIS) techniques and measurements of soils samples collected from various field sites. The results revealed that (1) a four period (1990, 2000, 2010, and 2020) RS dataset on soil salinization allowed for the accurate classification of the land use/land cover types, with an overall classification accuracy of greater than 90% and kappa values of >0.90, and the salt index (SI), an RS-derived risk factor of soil salinization, was significantly correlated with the actual measured salt content of the surface soils. (2) The RS-derived elevation and normalized difference vegetation index (NDVI) were significantly correlated with the SI-T. (3) An integrated risk assessment model was constructed for the soil salinization risk in the Bosten Lake watershed, which calculated the integrated risk index values and classified them into four risk levels: low risk, medium risk, high risk, and extremely high risk. (4) Due to the combined effect of the surface water area and terrain, the soil salinization risk gradually decreased from the lake to the surrounding areas, while the corresponding spatial range increased in order of decreasing risk. The areas with different levels of soil salinization risk in the study area during the last 30 years were ranked in decreasing order of medium risk > high risk > extremely high risk > low risk. These findings provide theoretical support for preventing and controlling soil salinization and promoting agricultural production in the study area.

Comprehensive Analysis of Nitrogen Deposition in Urban Ecosystem: A Case Study of Xiamen City, China

Atmospheric nitrogen deposition can supply nitrogen for ecosystems while posing a serious threat to ecological security. An assessment of the ecological risks caused by atmospheric nitrogen deposition is critical for urban sustainable development. Based on “Ecological Risk Analysis” and the “Driver-Pressure-State-Response (DPSR) framework,” this paper established a comprehensive ecological risk assessment model and assessed the ecological risk of nitrogen deposition in Xiamen City, China. The results showed that the risk from nitrogen deposition to the forest ecosystem is high due to the impact of nitrogen deposition on the residual rate of litter and survival rate of seedlings. The risks to freshwater and marine ecosystems were determined to be high and moderate, respectively, due to the promotion of eutrophication by nitrogen. The risk to farm ecosystems was low due to the impact on weeds. The proportion of high-risk areas in Xiamen City was 37.1%. Among the districts of Xiamen City, Tong’an and Xiang’an had the highest proportion of high-risk areas (48%) and low-risk areas (31.8%), respectively.

A simple model for screening the local impacts of atmospheric ammonia

The dry deposition of ammonia from the atmosphere to the surface can lead to eutrophication of sensitive ecosystems and acidification of the soil. A large proportion of the ammonia emitted from agricultural sources can be deposited within a few kilometres and, therefore, impacts of ammonia dry deposition often occur near to the source. To assess these impacts, short-range atmospheric dispersion models are often applied to simulate the emission, dispersion and deposition of ammonia. However, these models can be time-consuming to run and often require detailed input data and, therefore, for multiple assessments it is useful to have a method of screening to discard scenarios where impacts are expected to be negligible. The SCAIL model (Simple Calculation of Ammonia Impact Limits) has been developed for this purpose. SCAIL estimates the atmospheric concentration and dry deposition at the nearest edge of a sensitive ecosystem (receptor) downwind of an ammonia source. These estimates are calculated based on simple meteorological data, the emission rate of the source, land cover type and distance to the receptor. Analysis of the model predictions showed that uncertainty in the model input data leads to an uncertainty in concentration and dry deposition estimates of 25-30% and 40-45% respectively. Detailed atmospheric dispersion models will also have similar uncertainties since they use similar types of input data. Comparison of the concentration predictions with previous measurements made around eight farms showed that the model significantly underestimated concentrations although the model performance was similar to existing screening techniques. The measurement dataset was used to calibrate the SCAIL model which subsequently performed better, using independent verification data, than existing models calibrated in a similar way. The benefits of the SCAIL model are already being seen in the UK, where it is used to screen farms for potential impacts on statutory nature conservation areas.

Modeling the potential influence of particle deposition on the accumulation of organic contaminants by submerged aquatic vegetation

Submerged aquatic vegetation can act as both a mitigating factor(e.g., reducing downstream impacts of pesticides following runoff/spray drift) and mobilizing factor (e.g., remobilization of contaminants from sediments) influencing the fate and distribution of organic contaminants in the environment. Consequently, there has been wide scientific and regulatory interest in assessing the role of these plants in different contamination scenarios. Mechanistic models describing the environmental fate of contaminants in submerged aquatic vegetation are useful tools for interpreting laboratory and field measurements in addition to providing valuable information to risk assessors. In this study, we developed a fugacity-based model to investigate the influence of particle deposition to plant surfaces on the fate and distribution of two substances in small ponds. The main motivation for conducting this study was to address the fact that the potential contribution of this process is not typically considered by many types of models describing contaminant dynamics in submerged aquatic vegetation. For the hydrophobic substance included in this evaluation (lambda-cyhalothrin), model performance was greatly improved by including this process. The model was also applied in a generic context to compare the importance of particle deposition versus directwater uptake as a function of chemical properties (log Kow) and concentration of suspended solids in the water column. The generic application demonstrated that contaminant mass transfer is dominated by particle deposition for chemicals with log Kow greater than approximately 5.5--6 across a wide range of suspended solid concentrations and can be important even for low log Kow substances in some circumstances. Further empirical and modeling studies are recommended to explore this process more comprehensively.

Calculation of environmental concentration and comparison of output for existing chemicals using regional multimedia modeling

The environmental fate of 40 existing chemicals is discussed using the EUSES multimedia distribution and risk assessment model with site-specific parameter setting in an urban area of Japan including a highly industrial region. There has been a strong need to assess the environmental fate of a huge number of existing chemicals. Data on the emission amounts of chemicals are essential for such prediction, and PRTR surveys may yield this data. The study delivered the following results: (1) Volatile compounds with large amounts of emission showed higher predicted concentrations in air, and the concentrations of several compounds agreed well with averaged monitoring data within an order of magnitude. (2) A close relationship was found between the concentration of water and that of sediment, suggesting that the fate of chemicals in sediment essentially depended on the water environment. (3) A group of volatile solvents had high mass distribution ratio to air. Some compounds having high solubility in water were also included in that group due to the high ratio of air emission. Highly hydrophobic compounds with logK(OW) larger than 6.0 showed a high distribution ratio to soil and sediment. (4) Volatile compounds were mostly taken through air. The exposure through fish is a dominant pathway for highly hydrophobic compounds. (5) Exposure ratio could be gathered from physicochemical properties. The exposure from fish intake was roughly estimated by logK(OW), whereas exposure from air and water intake was difficult to estimate simply by vapor pressure and solubility in water, respectively.

Fate modeling of phenanthrene with regional variation in Tianjin, China

A multimedia fate model with spatially resolved air and soil phases was developed and evaluated. The model was used for calculation of phenanthrene concentrations in air, water, soil, and sediment in Tianjin area and transport fluxes between the adjacent bulk phases under steady-state assumption. Both air and soil phases were divided into 3113 individual compartments of 4 km2 each to assess the spatial variation of phenanthrene concentrations and fluxes. Independently measured phenanthrene concentrations in air, water, and soil were used for model validation. The spatial variation in soil was validated using a set of measured phenanthrene concentrations of 188 surface soil samples collected from the area. Most data used either for model calculation or for model validation were collected during the last 5 years. As the results of the model validation, the calculated mean values for phenanthrene concentrations in various bulk phases are in fair agreement with those independently observed and are very close to those calculated using the model without spatial variation. The absolute difference between the calculated and the measured mean concentrations are 0.14, 0.48, and 0.13 log-units (mol/m3) for air, water, and soil, respectively. The spatial distribution patterns of phenanthrene in both air and soil were well modeled. Spatially, however, the model overestimated the soil phenanthrene level at low concentration range and underestimated it at high concentration range. The calculated distribution of phenanthrene in the air matches well with the emission from fossil fuel combustion, while the calculated distribution pattern in the soil is similar to that observed.